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Your Premise is Terrible

Continuing the apparent tradition set forth by others, I figure I’ll share my thoughts on a TV ad campaign that I personally can’t stand: a set of ads for the Sony PlayStation 3. Each 30-second spot highlights some aspect of the game console — either the product’s capabilities, the selection of games available, or the reduced price of the unit. And they all follow a fairly rigid formula which, when you really sit down and think about it, doesn’t make a lick of sense.

Scarlett

Each commercial starts with a character in their home. “Dear PlayStation,” they always start. Then comes a brief description of some issue or question. “My girlfriend can’t tell the difference between a video game and a movie.” “My boyfriend hasn’t hooked the console to the Internet yet.” “We’re a shadowy and mysterious government agency who wants to purchase weapons we found inside one of your games despite the fact that common sense dictates that such weapons clearly violate several laws of physics.” You know, real important stuff. A two-line lower third graphic shows the name of the person along with a description of what they are and why they’re speaking — Desperate Father, Good Boyfriend, Unsatisfied Girlfriend, and so forth.

Kevin

Cut to a man in an office. The lower third here reveals that his name is Kevin Butler, but his job title changes from commercial to commercial. He generally holds an important position such as Director or VP, but always presides over some trivially stupid department — Big Action Moments, Epic Footage, Enough is Enough… You know, departments that don’t really exist. Anywhere.

Alright, so far we have a “customer” addressing the camera, followed by a response from Kevin Butler. The customer is at home, Kevin is at the office. So logically, this must be some kind of video mail. You know… Somebody asks a question on video, sends it off to the company, and they watch it and come up with an answer for you. Okay, simple enough, I’ll buy that. But wait. After Kevin speaks, we cut back to the customer. They add something, then it’s back to Kevin. What the hell? Are they having a conversation in real-time?

You see, this is where it happens. This is the moment when I stop accepting the reality of the world the commercial has tried to build. The moment I become acutely aware that I’m watching a commercial.

In most forms of performed narrative, like movies and theatre, there’s a thing the creators strive for called “suspension of disbelief.” When you sit in a movie theater and watch a superhero flying without any airfoils or logical sources of propulsion, or witness a globe-trotting adventurer knock a gun out of somebody’s hand with nothing more than a bullwhip, you blindly accept what you see. If you saw any of these things happen in your normal life, you’d think they were extraordinarily strange, and perhaps even slightly horrifying. Yet when it happens in a film, you can set all that aside and let the imaginary world guide you wherever it wants. What’s more, in a good film, you actually find yourself being drawn away from your real world. You forget about all the assholes sitting near you in the theater, you forget about the fact that you’re sitting in a sticky chair facing a gigantic screen, you forget that the whole experience is just a projected visual illusion. When it ends, you can’t intuitively tell exactly how long you were in there. You find it a little surprising that you had been in the theater long enough for the sun to go down. If the main character in the film experienced a happy ending, you find yourself in a good mood too (at least until the grim reality of your miserable life sets back in). A well-made film or a good play can take the audience completely out of the real world and sell them whatever fantasy the creators can dream up. That’s part of the magic of the medium.

As you can imagine, sometimes it can be a challenge to keep the audience in your world. Back when film was in its infancy, and no editing techniques had been established, some people thought that the act of cutting from one piece of footage to another would be so jarring to an audience that it would make them physically sick with confusion. In the ’40s and ’50s, it was thought that something as simple as a lens flare would remind the viewers that they were watching an imagined world through a camera lens and not experiencing something real with their own eyes. Nowadays we know pretty well the limits of what we can get away with. Audiences will generally tolerate all but the most choppily edited footage. Minor errors in continuity can also be glossed over by most people. The use of phenomena that don’t naturally occur in the human eye (like lens flare) actually became something of a visual style, albeit terribly overused. In fact, audiences don’t even consciously notice specks of water or dirt getting on the lens following some large action, provided it doesn’t stay there in subsequent shots.

In terms of keeping an audience in your world, one of the most damaging things you could possibly do is acknowledge that the audience is there. Think of practically every narrative you’ve ever seen. In the film’s world, the audience simply does not exist. Would she have cheated on her husband if she knew there was a theater full of people watching her affair? Would an action hero show his private moments of weakness if he knew we were all there? Don’t you think that small family dinner would be slightly different if they knew there were 200 audience members in their house? To keep the imaginary world from collapsing, the characters can’t talk about the fact that they’re inside a fake movie universe. An actor in a film with Al Pacino can’t ever hint at the fact that Al was once Michael Corleone or Frank Serpico… as far as the other characters know, he is only the character he is currently playing. That’s all he’s ever been, and that’s all he will ever be. And most importantly, nobody can look at the camera. Ever. If a character makes eye contact with the camera, they’re ultimately making eye contact with the audience. In a sense, the audience becomes a participant in the scene instead of an unseen voyeur watching stealthily. And that triggers confusion, surprise, guilt, panic, and a whole range of emotions you don’t want an audience to feel.

It’s called “breaking the fourth wall,” and it’s something that you never want to do. But wait! I can hear some of you prattling off lists of movies where characters do it. Ferris Bueller did it on his day off. Bluto looked at the camera in Animal House. When does THIS happen in the movie?And an entire set of scenes in Spaceballs revolved around the characters fully comprehending that they were in a movie. Why do they get away with it? Well you see, they were doing it for comedic effect. The entire point was to use the audiences’ confusion and discomfort to get some laughs. It’s incredibly difficult to do without coming across as “hokey,” and even harder to recover from. I mean, nobody watches Spaceballs and thinks, “Well if Dark Helmet understands that he’s just acting for a movie, then why does he play with dolls later in the film? Certainly he must know that we’re all watching.” Nobody’s that analytical, especially when there’s a funny movie playing in front of them. And it’s not like these films are going to be winning any Best Picture awards. Addressing the camera is just something you shouldn’t do if you’re trying to be taken seriously at all. To further confound things, you can actually strengthen the imaginary world by alluding to the fact that you could be in a movie or TV show, without actually acknowledging that you are. This clip from the Scrubs episode “My House” demonstrates it perfectly (until the copyright holders pull it, anyway)… Dr. Cox explains that they aren’t in a TV show, and there aren’t any cameras filming them, and we as an audience choose to believe that he’s telling the truth — despite the fact that those facts are clearly, demonstrably false. If he had pointed into the actual camera, or if anybody in the scene gave the lens even a passing glance, the illusion would be ruined. It would have changed from an impassioned, believable speech into a hollow, meaningless setup for a cheap joke. And while there are times and places where that’s appropriate, most of the time it just flat-out isn’t something you should do.

But not all the world’s a narrative. In a news program, the anchors speak to the camera pretty much exclusively because they’re actively trying to talk to the audience. In fact, unless they’re interviewing somebody or engaging in stupid banter with Bob the Weatherman, it’s an ugly mistake to talk anywhere but to the live camera. Same with a documentary host, if they’re trying to “bookend” some portion of their story. Every episode of Dirty Jobs or MythBusters starts with the hosts looking at the camera, talking to us. In these types of shows, it doesn’t matter that the audience isn’t drawn into the world because their world isn’t imaginary. It doesn’t take a huge stretch to accept that somebody was stabbed in Brooklyn, or that pig breeding might not be the most pleasant career path you could choose. But at the same time, you don’t feel as connected to the presenters. If you watch Mike Rowe trudge through a sewer pipe, you tend to care less than you do about watching Andy doing the same thing in The Shawshank Redemption. It doesn’t matter that Mike was doing it for real, the scene doesn’t hit you on the same emotional level as the film equivalent.

So what does this have to do with commercials? Well, most commercials tend to fall into one of the categories explained above. Either the commercial is built from fake people believing fully in their fake world while the unseen audience watches, or it features somebody the audience can relate to who addresses the camera and informs you about his miracle drug cure for frequent urination. A good narrative example would be this Domino’s ad that ran a little while ago: Clearly, they want you to accept the fact that nobody in this commercial realized that they’re actually in one. They act naturally, don’t acknowledge or speak to the audience, and they go about their business. (Although I could go on a whole other tangent about the guy’s wardrobe discontinuity, and how that doesn’t make a whole lot of sense either.) The opposite would be something like, oh I don’t know, the classic Liberty Medical commercial where Wilford Brimley keeps saying “diabeetus.” He’s talking to you about the ‘beetus. That’s why he invited you into his living room, you see?

Okay, so how do you classify the PlayStation commercials? Well, the characters are addressing the camera directly, so you can throw narrative straight out. But they’re not talking to “us” as an audience either; they’re talking directly to one another. But they’re looking at us. The only logical way to interpret the premise is as follows… The “normal people” are having their questions filmed so that Kevin can offer answers directly. Kind of like on shows that have the concept of viewer mail. Somebody asked a question, and the hosts answer it to the audience as a whole, knowing that it will eventually get to the person who originally asked the question. But the problem with that concept is the fact that it’s asynchronous. You have a question, and you either write it down on a letter or encode it to a videotape. Then it has to go off to the host who reviews it, maybe thinks about the answer for a while, and eventually gets on camera and offers an answer. The round-trip time is enormous. You pretty much get to fire off one question, and you eventually get one answer. If you need clarification or a follow-up, chances are you won’t get it. Or it at least won’t be televised, because by the time the second question got to air, everybody will have forgotten the first one. But the characters have a speedy back-and-forth. So what’s happening here?

Maybe they’re having an online video chat. Seems unlikely though, because as that cursèd E-Trade baby taught us, online video (and generally, all user-generated video) is poorly framed, sloppily lit, with no attention paid to audio and with way too low a bitrate. And whether you’re doing a video podcast or a live one-on-one chat, the participants always look like they’re sitting in front of a computer. Again, not the case in the PlayStation commercials. They exhibit good lighting and decent camera work (if you ignore the fact that they’re doing the faux-documentary “shaky cam” move, a personal pet peeve of mine). In fact, some of the commercials even feature dolly moves — definitely not something you’d do with a webcam.

The first commercial that aired in the series even featured an edit during the initial question. It starts on a wide, establishing shot for the “Dear PlayStation” line, then cuts to a much tighter shot as the question begins. None of the other ads in the series seem to do that. Perhaps they needed the larger frame to show the slightly cluttered, yet uncharacteristically upscale room that Bernie is in. Or maybe they abandoned the convention in later commercials, much in the same way they stopped referring to the callers by name. (Kevin calls Bernie by his name, but he never does it again in any subsequent commercials.) To further complicate the world this campaign has built, in the Desperate Dad spot, somehow Kevin abducts Aaron’s entire family, much to his confusion.

(Also, as an aside, why do all these commercials start with the line “Dear PlayStation”? PlayStation is a product, yet the characters are most likely trying to contact the company that makes the product. Sony, in this case. Starting a letter to Sony with the words “Dear PlayStation” would be like me writing a letter to Microsoft saying “Dear Windows XP Professional” or writing a letter to Ford saying “Dear poorly manufactured 1998 Contour.” Which is actually pretty funny.)

So then what the hell is it? Well, somehow they’re having a live, one-on-one conversation through some device that transmits extremely high-grade, professionally produced video and audio. And for whatever reason, everybody who wants to ask a question has one of these and knows how to use it to call Kevin Butler. In short, it’s something that simply doesn’t exist. And since the commercial doesn’t give you enough time to suspend your disbelief, this ends up being all I can think about when one of these commercials airs. The commercial supposedly has one purpose: to entice me, and make me want to run out and purchase a PlayStation. And yet all I can do is sit there and ask “How the hell are these two people talking?” The commercial failed.

Not only did the commercial fail to grab my wallet, it also opened itself up for a meticulous deconstruction. Just like it’s hard to find faults in your favorite movie, it’s frighteningly easy to pick apart something you feel to be a piece of crap. So I did. I took the first commercial, apparently named “It Only Does Everything,” and let loose.

While trying to dig the video up online, I learned one thing: Nigerians are pissed about it. Which I find to be a little bit funny, because I didn’t even know they had televisions or Internet connections. I kid, I kid. But yes, the line “… you can’t believe everything you read on the Internet. Otherwise I’d be a Nigerian millionaire by now” really rubbed some people the wrong way, despite the fact that it’s pretty much innocuous. I mean, it’s not like the commercial is really doing anything to damage Nigeria’s reputation. That damage has already been done by the flood of spam messages that we’ve all seen over the years promising millions of dollars from Nigerian princes. Maybe they should’ve signed petitions to stop that instead.

But never underestimate the power of angry, indignant people. The offending line was eventually changed to “… you can’t believe everything you read on the Internet. That’s how World War I got started.” They traded a decent (albeit slightly offensive) Internet culture quip for a non sequitur about the Internet causing WWI. Check it:

Nigerian Millionaire
World War I

Personally, I like the Nigerian one better because at least it tried to be clever. The WWI joke really just feels like somebody shoehorned it in there because they couldn’t think of anything that fit better. Notice how similar the two different jokes are visually. Same set, same props, same actors, roughly the same camera move… They obviously shot both those jokes together at pretty much the same time, and probably a few more that never saw the light of day. Maybe they knew that the Nigerians would hate the original joke and they chose to do backups. Maybe they didn’t know what joke to go with, so they shot a bunch and had a focus group decide what they liked best. But Nigerian complaints didn’t bring the cast and crew back together to reshoot a new joke; they always had other jokes lying around “just in case.” If they didn’t shoot a spare joke, it would’ve been a lot cheaper to just pull the ad and hope the world would forget about it.

Also, you notice that brief, less-than-one-second cut back to Bernie after the joke in question? The one where he makes a stupid face in response to Kevin’s line? It’s a band-aid, and it looks and feels slightly out-of-place. The joke, as well as the description that directly follows it, should probably be one continuous shot. And for all we know, at one point it may have been. But when the editor replaced one joke with another, it created an awkward cut where everything on the screen seems to “jump” from one place to another. Maybe the camera’s a little further back, or Kevin’s more to the left, whatever… You can’t stitch two different takes together without having something to cut away to. And in this case, that something was that little piece of Bernie (as out-of-place as it may seem).

And am I the only one who thinks Bernie is an unbearably unlikeable character? He’s like one of those people I had to constantly fight to escape from in college. The kind of person who spends all day, you know, playing games. Or running online forums devoted to talking about playing games. The kind of person who hates having to go to his sister’s wedding because it eats up valuable time that could be spent online speculating how much the new PlayStation is going to cost. Everybody knows a guy like this. Somebody whose online handle is something inane like DwarfHammerMage86. Somebody with a lateral lisp who smells like Fritos. Somebody who knows how to speak Klingon but can’t bring himself to look a woman in the eye. You know, Bernie. Oh, and by the way, I’m convinced that is not Bernie’s room. No, no. Not by a long shot. Bernie’s room is lined with posters of game characters and anime babes. It’s littered with collectible pewter figurines and comic books. The window shades have been closed for the past three years because he doesn’t want his parents to come home from work and catch a glimpse of him watching hentai (again). No, I’m convinced Bernie must have snuck up into his father’s office while nobody was around to film this commercial. And what’s with the arrogant tone of his voice? It’s like he thinks he’s entitled to an answer. I can’t find any remotely redeeming quality about the character. Not even in the Jonah Hill sense, where you at least feel sorry for the poor guy… No, there’s nothing here that’s remotely worthy of my emotional interest.

You want more? Okay, how about in the end graphics, where they briefly spell “Blu-ray” wrong? Oh yes:

Blue Ray
Your mother must be so proud.

Now, look, I’m not claiming I’m perfect. I’m not going to go around boasting that I’ve never made a typo while inputting text into a character generator, because I have. Everybody has. But I can certainly say that I’ve never made that mistake on something with as big a budget as I’m sure this campaign had. When you reach a certain level in the industry, you double-check everything you type into somebody else’s video. It’s not just a professional courtesy, it also ensures that you don’t end up looking like some idiot who doesn’t pay attention to their work. And it also ensures that there will still be a job for you to come back to tomorrow morning.

So there it is, in all its ugly glory. I hate these commercials, and I’m not afraid to come forward and say it. If you ever find yourself in a brainstorming session for a commercial shoot, you may want to keep a few of these points in mind. Otherwise I’ll have to spend yet another evening complaining about your terrible premise as well.

1 Comment Posted Sun, 06-Dec-2009 at 7:26:42 pm in Boring Technobabble

(This Song’s Just) 76,482 Tiles Long

Last month, I assembled and uploaded a video showing a timelapse road trip from Pittsburgh, PA to San Mateo, CA. I’ve had a lot of requests from people (two, actually) who wanted to know how I managed to put the video together. So I’ll share the tricks and pitfalls I discovered in my work.

The data came entirely from my friend Angelo. He ran the InstaMapper app on his iPhone for the majority of the trip. It ran sporadically during the first day, and it occasionally needed to be turned off throughout the journey so he could use the phone functions. But for a large part of the trip, it sampled the date/time, latitude, longitude, altitude, speed, and heading every 30 seconds. Each point was uploaded to InstaMapper’s servers, where friends and family could go online and track our progress in real-time.

Angelo put together a few map overlays and some graphs of our speed over time, but I thought I could do one better. So I asked him to export all the trip’s points in a CSV file, and set to work trying to pull something out of it. The data file looked like this:

...
2009-06-03 12:30:36,N 39.06617°,W 96.66934°,1014,66,268
2009-06-03 12:31:06,N 39.06549°,W 96.68029°,1014,73,266
2009-06-03 12:31:36,N 39.06484°,W 96.69175°,1033,70,264
2009-06-03 12:32:07,N 39.06393°,W 96.70280°,1053,73,262
2009-06-03 12:32:37,N 39.06283°,W 96.71413°,1082,73,262
...

And it continued in that fashion for 3,645 lines. As I was writing a PHP skeleton to read and loop over the data, it hit me: I should pass the points to Google’s Street View service, scrape the images, and put them all together into a timelapse video. At the time, I hadn’t planned any further than that. I didn’t know how the Street View site worked, I didn’t know if it was even technically possible to scrape the images in this way, and I wasn’t even sure if the data points were frequent or consistent enough to even make an intelligible video that didn’t just look like a mash of crap. In fact, I didn’t even know if Google had Street View tiles for a lot of these stretches of road. I mean, seriously, who needs to see what Route 40 in Kansas looks like?

Not knowing where else to start, I took a shot in the dark. I put one of the latitude/longitude pairs into Google Maps’ search box, opened Wireshark, and turned on Street View.

Right off the bat, I started to understand how it worked. The first request was for http://maps.google.com/cbk?output=xml&ll=latitude,longitude. After that, a bunch of requests went out for http://maps.google.com/cbk?output=tile&panoid=mess_of_hex&zoom=3&x=something&y=something as I panned the applet around and images loaded.

The first URL was easy to decipher. I simply had to take the latitude/longitude pair from each data point, convert it from the °N/S format into a pair of +/- numbers, and substitute it into the URL. What came back looked something like this:

<panorama>
  <data_properties image_width="3328" image_height="1664" tile_width="512" tile_height="512" pano_id="AayUS0rw0WbPyJnkbJ8v1Q" num_zoom_levels="3" lat="38.878531" lng="-97.938661">
    <copyright>© 2009 Google</copyright>
    <text>I-70</text>
    <region>Madison, KS</region>
    <country>United States</country>
  </data_properties>
  <projection_properties projection_type="spherical" pano_yaw_deg="268.49" tilt_yaw_deg="33.20" tilt_pitch_deg="2.18"/>
  <annotation_properties>
    <link yaw_deg="88.36" pano_id="pyOJkOLMpadaGJviyoQbJw" road_argb="0x80f2bf24">
      <link_text>I-70</link_text>
    </link>
    <link yaw_deg="268.36" pano_id="WJiMEx8Zdbg7QlPS3sMsFw" road_argb="0x80f2bf24">
      <link_text>I-70</link_text>
    </link>
  </annotation_properties>
</panorama>

Things were looking good. I was able to get the name of the road, the city, state, and country easily from every data point. Another stroke of good fortune came in the form of the pano_id attribute, which turned out to be the single identifying name of every panorama in Google’s systems. And sure enough, the “mess_of_hex” I saw in my Wireshark capture turned out to be the pano_id from this file. Step 1 was complete.

I learned later that if you have a latitude/longitude pair that Google doesn’t have Street View data for, it will return a much emptier XML tree. If I saw pano_id was missing, I just skipped over that point and went on to the next one.

I turned my attention to the second URL. I knew the meaning of the first argument, but I was unsure what x and y were referring to. So I put zeros into both of them. What came back was a 512×512 pixel image containing nothing but sky. I tweaked x and y and realized that the panoramas are split up into square tiles. In this particular location, valid x values were 0 to 6. Valid y values were 0 to 2. Anything beyond that, and an image of solid black was returned. In total, the panorama was made of a 7×3 matrix of 512×512 images, for a grand total of 3584×1536. Loop through all the x and y pairs, save the images to disk, and we have step 2. The zoom attribute seems to work well at 3. I haven’t found any points where a value ≥4 works, and zoom levels 1 and 2 make the image smaller (and consequently, use fewer tiles). I stuck with zoom level 3, figuring that since I was doing all this, I may as well do the hell out of it.

Because each “frame” of the video was in 21 pieces, it needed to be stitched together. I found the laziest solution to be the montage utility from ImageMagick. To save you some time, the syntax of the command is montage [file list] -tile 7x3 -geometry +0+0 output_file.jpg, where file_list is a space-delimited list of the 21 images you want stitched. Make sure the source files are in the correct order, or you’ll get some wonky images out of it. There’s step 3.

The output files were numbered sequentially, and left-padded with zeros. (0001, 0002, 0003, etc.) I opened QuickTime and loaded all the stitched JPEGs as an image sequence. When asked for a frame rate, I picked 24 to see how it would look. My laptop’s hard drive could barely stream the image data fast enough, but I started to see things come together. It was starting to look like a video! I resized the image sequence to 1280×548 and saved it into a QuickTime .mov file using the Photo JPEG compressor. (The frame size was set to match the width of a 1280×720 HD image. The 548 height was a consequence of the panorama’s inherent aspect ratio.) Step 4 was complete.

At this point, I was relatively happy. My video looked pretty good, albeit a little jumpy and erratic. But a lot of that came from Google’s own imagery - there wasn’t much I could do about that. And theoretically I could’ve smoothed the data, interpolated the missing points, etc… But I wanted to stay true to the data as we captured it. It seemed more authentic that way.

The shape of the image bugged me, though. It filled up a 720P frame just fine width-wise, but there were 172 vertical pixels that were completely unused. I didn’t just want to letterbox or crop it into 16:9… As I was watching it for the fifth time, it occurred to me that I had no idea where I was as I watched the video. I could tell when I entered a large city, but I couldn’t tell which city it was. I needed captions.

So I whipped up another PHP script to pull the street/city/state info out of Google’s XML, combine it with a human-readable summary of the GPS data, and output it using GD functions into a 1280×172 JPEG image. I made one of these images for every video frame, and combined them into a Photo JPEG QuickTime movie using the same steps as the panoramas. I guess I’ll call that step 5.

I took these two movie files, loaded them into Adobe Premiere (unfortunately the only video editor I had with me on the road) and combined the two videos together. From there, I saved the whole thing as MPEG-4, uploaded it to YouTube, and the rest is history. Is that even worth being called step 6?

The first thing I noticed about the video is the inconsistency in some of Google’s imagery. For example, some of the areas around Denver, CO seem to be captured at a lower detail level. The image is smaller, aligned to the upper left corner, with the right and bottom areas of the frame surrounded by black. This causes a bizarre flicker in a few points. Likewise, there’s an area near Saline, MO where the images seem to have been captured in the dead of night. You can see headlights illuminating the road, as well as a huge amount of CCD noise which manifests itself as a colorful moiré pattern.

Another weird issue came about as a result of overpasses and service roads. Sometimes, a GPS point was captured just as we were driving under an overpass. The resulting street view panorama, at times, placed us on top of the overpass instead of driving beneath it. Likewise, sometimes the GPS is a little fuzzy, and it places us on a service road or some parallel street instead of the actual highway. I’m thinking this caused a few blank XML results.

In addition to that, I lamented the fact that the timestamp format used in InstaMapper’s CSV file is completely useless. It saves the time in the local format instead of UTC, and it doesn’t put the time zone anywhere in the data. Normally, this wouldn’t be a problem, because you could come up with some hack to work around it… Be it figuring out the time zone based on any backwards jump in time, or hammering on some geography website’s API to determine the time zone for a particular latitude/longitude pair. The real problem came from the fact that the iPhone’s clock is totally unreliable when crossing time zone borders. I don’t know if it’s Apple’s fault or AT&T’s, but at times it took six hours AND multiple reboots to get a time zone change to stick. In fact, when we went from Mountain to Pacific time, our iPhones actually went back to Central time for a little while! The timestamp that made it into the final video is simply whatever time the iPhone thought it was at the moment, as wrong as it may be.

Another gigantic issue occurred to me while I was generating the caption images… I never took into account the heading of the GPS data. I got the latitude and longitude, and the resulting video looked good enough based on that… Since the center of the image always faced straight down the road, I took for granted that the Street View car was driving the same direction we were. I later realized that, indeed, some of the panoramas were 180 degrees off. If you watch the clouds in the video, you’ll notice that at times they seem to change direction. It appears as though the projection_properties section of the XML contains critical information on how to properly center the image. This really complicates the stitching process, but it shouldn’t be insurmountable. It’s the sort of thing that would warrant a new revision of the video.

All in all, I’m proud of the way it all turned out. This basic idea could be expanded to generate automated trips down stretches of road, without being fed GPS points to follow. Without giving too much away, I will say that the <link ...> tags in the XML translate into the clickable road arrows that appear in the applet, and each link points to the pano_id directly before and after the panorama you’re looking at. In this video, I passed the first URL a set of latitude/longitude points for the ll= attribute… But you can provide a panoid= instead if that’s all you know. I’m not going to say any more about that. If you have the inclination, I’m sure you know how to put 2 and 2 together at this point.

Bear in mind that this is completely against Google’s terms of service. Not only is it an abuse of their (ample) bandwidth and machine power, but all these images are copyrighted. Having said that, I pulled down 3,645 XML files and over 76,000 image tiles in the course of a few hours, and they didn’t seem to notice. But as always, your mileage will vary.

Keep on truckin’.

No Comments Posted Tue, 21-Jul-2009 at 3:47:06 am in Boring Technobabble

Fun with YouTube’s Audio Content ID System

A marginally scientific analysis by Scott Smitelli. Emails? parallax@csh.rit.edu.

Anybody who hasn’t been living under a rock knows about YouTube. It’s a video site built entirely around user-submitted content. Anybody can film anything, upload it to the site, and anybody on the Internet can watch it if they so choose. Sounds great in theory, but over time it’s succumbed to a very basic problem: The users can’t be trusted.

Copyrighted material — TV shows, music videos, concerts, even entire feature films — have popped up on YouTube in huge quantities. Obviously, the copyright owners and content providers don’t like this, especially when such free distribution cuts into their bottom line. Back in the day, a copyright holder would have to stumble across an infringing video, contact YouTube, and ask them to take it down manually. It doesn’t take a genius to realize that the content providers couldn’t keep that up forever, especially as more and more new users kept pouring in.

Enter Fingerprinting

YouTube narrowly avoided legal trouble by promising the big media companies that they’d develop a system that could detect and automatically remove any copyrighted material that was uploaded to the site. But in reality, they didn’t actually develop the audio fingerprinting system; they licensed it from a company called Audible Magic.

Audible Magic originally wrote software for CD duplication companies. When you handed a master disc off to a duplication house, they’d check it with an Audible Magic system first. The goal was to positively identify every song on the disc, as well as the copyright/licensing status, before the company ran off 10,000 copies of your potentially pirated disc.

YouTube jumped at this technology and worked to integrate it into their site. It scanned over all the uploads and generated a “fingerprint” for each video. It would then compare each fingerprint to a database containing practically every copyrighted work that the media companies wanted to keep off the site. If any videos matched, it was assumed that the user has posted copyrighted material without permission and the infringing video was removed.

Some labels got the right idea, though. Instead of demanding that any infringing content be taken down, some chose to promote their material or insert links to pay music sites where you could purchase the songs that were being played. That was an amazing idea: It permitted the users to basically do whatever they wanted copyright-wise, while still driving traffic and potential sales to legitimate music retailers.

Heating Up

That worked well enough for a time, but the media companies weren’t satiated yet. A slew of legal threats, negotiations, and all-around chicanery ensued. After all, YouTube was making money by running ads alongside videos which often contained material from these companies, and they all wanted a piece.

Unfortunately, nothing seemed to please Warner Music Group, who left the talks without reaching an agreement. They then demanded that YouTube remove every single piece of WMG-owned media on the site. Videos disappeared all over the place.

This Is Where I Came In

I don’t consider myself to be much more than a casual YouTube user. I’ll upload maybe one or two things a year, but nothing amazing or anything I put any real effort into.

For example, one of my videos depicts three members of my high school’s marching band dressed in pajamas at an overly girly sleepover. The song used in the background was “I Know What Boys Like” by The Waitresses. I thought it was hilarious when I was 17, but I had all but forgotten about it five years later.

I was caught by surprise one day when I received an automated email from YouTube informing me that my video had a music rights issue and it was removed from the site. I didn’t really care.

Then a car commercial parody I made (arguably one of my better videos) was taken down because I used an unlicensed song. That pissed me off. I couldn’t easily go back and re-edit the video to remove the song, as the source media had long since been archived in a shoebox somewhere. And I couldn’t simply re-upload the video, as it got identified and taken down every time. I needed to find a way to outsmart the fingerprinter. I was angry and I had a lot of free time. Not a good combination.

I racked my brain trying to think of every possible audio manipulation that might get by the fingerprinter. I came up with an almost-scientific method for testing each modification, and I got to work.

Methodology

The song chosen for all the tests is “I Know What Boys Like,” a 1982 song by the one-hit wonder group The Waitresses. This song was chosen for several reasons:

  • It was the first song I ever saw that was identified and removed by YouTube’s fingerprinting system.
  • It has a very distinctive sound that I thought would be easily identifiable. It’s also really repetitive, which probably makes it an easy target for an automated system to detect.
  • It’s one of the few songs I actually have readily available in an uncompressed format. The majority of my music collection is stored with lossy data compression, which might have impacted the results.
  • In general, it’s just a terrible song. I wanted to highlight the fact that somewhere out there, somebody thinks this 27-year-old heap is still valuable enough to be barred from YouTube.

The song originally came from a 1990 CD pressing of “The Best of the Waitresses,” which I came across during my freshman year of college. I was so surprised to see a copy of this album, I begged the owner to allow me to make a copy for posterity (and also for hilarity). I used Nero Burning ROM to make a bit-perfect copy of the full album onto a CD-R. I then listened to my copy, laughed at the majority of it, then stored it in a CD binder.

Fast-forward to the present day, when I decided to run these tests. I ripped my copy of the album with Exact Audio Copy in “secure” mode. The result was a 16-bit stereo, 44,100 Hz PCM wave file. This was used as the master file for all the tests.

For each test, a duplicate copy of the master file was manipulated. Practically every change to the audio was made in Adobe Audition 3 on Windows. The modified duplicates were saved as 44,100/16 stereo waves and moved over to a Mac.

Each file was loaded into an empty Final Cut Pro sequence. The video settings, although theoretically irrelevant, were always set to 24 FPS, progressive, NTSC 720×480 @ 4:3, with 44.1/16 stereo downmix audio. The audio files were matched with a default Text generator which described the test being performed. The resulting video files were saved in DV NTSC QuickTime format.

From there, the files were moved into Apple Compressor where they were batch converted into a format YouTube would accept. I chose the “H.264 for iPod video and iPhone 320×240 (QVGA)” setting, which encodes reasonably fast with excellent quality. The final output files were M4V containers with H.264 video and AAC stereo audio.

Finally, the video files were uploaded to my YouTube test account. I chose the name retnirpregnif, which is the word “fingerprinter” backwards. The title of each uploaded video was always set to a description of that particular test. In all but one test, the description was set to ‘The song is “I Know What Boys Like” by The Waitresses.’ I chose that description to see if the presence or absence of a copyrighted song name in any of the metadata fields influenced the detection. The tags, category, and any other fields were left blank, and possibly auto-filled by the uploader.

I considered a test passed if the status line on my account’s “Uploaded Videos” page read “Live!” and the thumbnail had been generated. (Also, if the video actually played, that’s a big plus.) If a video had a status of “Matched third party content” or I received an email about a particular video, I considered that test failed.

Please note that these tests are only meant to test the AUDIO aspect of YouTube’s fingerprinting system. They probably have a similar feature in place to scan for content in the image data, but I make no effort to test that in this document. The video fingerprinter might be susceptible to tweaks like those I describe below, or it might be an entirely different can of worms. I’ll leave it to somebody else to figure that one out.

The Tests

No Description

For the first test, I uploaded a completely unmodified copy of the entire song, but with a description field that read “No Description.” The purpose of this test was to determine if YouTube could still identify the material if none of the user-submitted metadata gave any indication that it was there.

Reverse

The entire song was reversed. The purpose of this test was to determine how discriminating the fingerprinter was. If the test passed, it would reveal the systems inability to identify a song which is playing backwards.

Pitch Alteration

Pitch Alteration

The entire song was modified with Audition’s “Stretch” plugin. In all tests, the Precision was set to High, Constant Vowels was off, Preserve speech Characteristics was on, Formant Shift was 0, and Solo Instrument or Voice was on. (Admittedly, it should’ve been off, but that would’ve taken friggin’ forever to process.)

For these tests, the Stretching Mode was Pitch Shift. The Ratio was changed from test to test to create varying amounts of pitch change.

These tests created an output file with exactly the same length and speed as the source, but with the pitch increased or decreased. These tests were designed to determine if the fingerprinter looks at the “notes” the song is made of.

Time Alteration

Time Alteration

The entire song was modified with Audition’s “Stretch” plugin. In all tests, the Precision was set to High, Constant Vowels was off, Preserve speech Characteristics was on, Formant Shift was 0, and Solo Instrument or Voice was on.

For these tests, the Stretching Mode was Time Stretch. The Ratio was changed from test to test to create varying amounts of tempo change.

These tests created an output file with exactly the same notes as the source, but with the speed (tempo) increased or decreased. These tests were designed to determine if the fingerprinter looks at the “beats” and rhythm of the song.

Resampling

Resampling

The entire song was modified with Audition’s “Stretch” plugin. In all tests, the Precision was set to High, and Constant Vowels was off.

For these tests, the Stretching Mode was Resample. The Ratio was changed from test to test to create varying amounts of tempo change.

These tests created an output file with both altered pitch and altered speed relative to the original. Quite simply, the song was played back at a faster or slower rate than the original — similar to a tape being played at the wrong speed. And now I suddenly feel old.

Noise

The entire song was mixed with varying levels of background noise. In the first round of tests, the song was mixed with varying levels of pure white noise created with Audition’s Noise generator (Color=White, Style=Independent Channels, Intensity=40).

For the second round of tests, the entire song was played on a set of M-Audio BX5a studio monitor speakers (chosen because of their flat frequency response ≥100 Hz, and because they were the only ones I really had available), and recorded into a Canon ZR200 camcorder onto a MiniDV tape. The tape was captured into Final Cut Pro, the resulting 48,000 Hz 16-bit audio was split off to a wave file, and then it was converted back into 44,100 Hz in Audition. The camera was placed at different distances and different angles relative to the stereo field’s central axis. No effort was made to keep the room quiet during the trials, and as a result things like heaters, refrigerators, TV flyback transformers, and running water can be heard throughout.

Amplification/Attenuation/DC Bias

Amplification/Attenuation/DC Bias

The entire song had its volume adjusted by varying amounts from test to test. For amplification tests, the song was allowed to clip hard at 0 dB, creating a great deal of distortion on the louder trials.

In later tests, the amplification was unchanged, but a positive DC bias was added to the signal, resulting in a great deal of distortion and the type of audio I’m afraid to play on good speakers.

These tests were designed to see if there was any absolute volume below which the fingerprinter couldn’t detect the song. Likewise, it tested to see if any amount of digital clipping and distortion could disrupt the detection process.

Time Chunks

Time Chunks

The song was trimmed to (n * 3) seconds long, where n is a value that changes from test to test. The kept segment of audio comes from near (but not exactly) the center of the song. From 0 seconds to n seconds, the audio is muted. Likewise, from (n * 2) seconds to the end of the song, the audio is also muted. The resulting n seconds at the center of the song are allowed to play. If the song is shorter than (n * 3) seconds, the muted sections are shortened so the entire output file is the same length as the source.

In later tests, the muted and unmuted portions were aligned to the head and tail ends of the song, for reasons that will be explained later.

The goal of these tests was to determine how much of the song needed to be present to trigger a positive detection, and if the position of that section had any effect on the detection.

Stereo Imagery

Stereo Imagery

The entire song was subjected to a series of filters that modify the audio based on the similarities and differences between the two audio channels.

For two of the tests, the vocals were removed or isolated using Audition’s Center Channel Extractor plugin (Extract Audio From=0° phase / 0% pan / 0ms delay, Frequency Range=140-20,000Hz, Volume Boost Mode=off, Crossover=100%, Phase Discrimination=4.5°, Amplitude Discrimination=6dB, Amplitude Bandwidth=9dB, Spectral Decay Rate=0%, FFT Size=32,768, Overlays=12, Window Width=100%).

In the third test, both channels’ waves were inverted. The phase relationship between left and right were preserved.

In the fourth test, only the right channel’s wave was inverted. The left remained untouched. The resulting audio file is completely out-of-phase.

In the fifth test, the two channels were first averaged together, effectively making the file mono. It still had two channels, but they contained identical waveforms. The right channel was then taken out-of-phase in the same manner as the fourth test. The resulting audio file is completely out-of-phase, and when both channels are summed together, they will destroy one another and average out to zero, or total silence.

These tests are designed to see how well the fingerprinter copes with audio with unexpected phase alterations. Also, the later tests attempt to reveal if the fingerprinter considers the files in stereo, or if it first converts them into mono for analysis.

The Results

No Description

Video Modification Performed Result
FxRVPFT5BKM Description field left empty FAIL

Reverse

Video Modification Performed Result
dYQn6CW4-VI Song was reversed PASS

Pitch Alteration

Video Modification Performed Result
7AXCbscz5Ys Pitch was lowered 25% PASS
sE27S6bDocw Pitch was lowered 10% PASS
6EBUGz_k5ww Pitch was lowered 7% PASS
9m5Z2p2P7YE Pitch was lowered 6% PASS
U9FlSevqiA0 Pitch was lowered 5% FAIL
xMlx7rdpP0I Pitch was raised 5% FAIL
rtL0DyACoz0 Pitch was raised 6% PASS
7b_Zy2M9iWU Pitch was raised 7% PASS
mkm0fIuDOOA Pitch was raised 10% PASS
RudFV8HlfAI Pitch was raised 25% PASS

Time Alteration

Video Modification Performed Result
vIJhocJdCJg Time was expanded 25% PASS
WgYeFcSl-bs Time was expanded 10% PASS
Z8IUZCceVXc Time was expanded 7% PASS
yo8Kg0Ws6zk Time was expanded 6% PASS
mfNYWnZ9P9M Time was expanded 5% FAIL
e2eg7JsVk2o Time was compressed 5% FAIL
1KLRmCJgcwQ Time was compressed 6% PASS
HhgN2Y7ICgU Time was compressed 7% PASS
SDw7JvrfvIg Time was compressed 10% PASS
S0C-0RxVe2Y Time was compressed 25% PASS

Resampling

Video Modification Performed Result
HuM_gTA-Id8 Song was slowed down 25% PASS
T5StunpZUeo Song was slowed down 5% PASS
6iFBNPpteog Song was slowed down 4% PASS
lMxy9ZUXXT0 Song was slowed down 3% FAIL
LSTj5LhWIwY Song was sped up 3% FAIL
Lzt4wIWWTz8 Song was sped up 4% FAIL
S-Q7uJ3ErTw Song was sped up 5% PASS
_v82g7p2Qio Song was sped up 25% PASS

Noise

Video Modification Performed Result
nHAdiiFsSwI 0% white noise, 100% song FAIL
V-sOEWQyKyI 25% white noise, 75% song FAIL
R1tRH0vBaT4 40% white noise, 60% song FAIL
-XF2DYIku18 43% white noise, 57% song FAIL
yuHWKDmItLQ 44% white noise, 56% song FAIL
TCOq67P2-f0 45% white noise, 55% song PASS
5jQqMd07yQk 50% white noise, 50% song PASS
2wAHwKBIqPY 75% white noise, 25% song PASS
w4fXEoJ1Tyw 100% white noise, 0% song PASS
YnB82m8AA4U Camera 5′ away, 0° off-axis FAIL
DBEicY5e2xI Camera 12′ away, approx. 45° off-axis PASS
y7duUWMSIUM Camera 31′ away, 90° off-axis FAIL
LiR0we4xugk Camera in next room PASS

Amplification/Attenuation/DC Bias

Video Modification Performed Result
dOzESZj75OM Volume was reduced 48 dB FAIL
TAZCzTj2ICc Volume was reduced 24 dB FAIL
PnOcheExAoA Volume was reduced 18 dB FAIL
FjbBPv-jwfo Volume was reduced 12 dB FAIL
7Zrp4FLEtV8 Volume was reduced 6 dB FAIL
0cA3Ngsp2Tc Volume was increased 6 dB FAIL
b06I160C1RE Volume was increased 12 dB FAIL
l3ee8Mcemf4 Volume was increased 18 dB FAIL
HnsPvUIs5Jc Volume was increased 24 dB FAIL
M6eoLBjRaHE Volume was increased 48 dB FAIL
uE3Tl5uEWI4 50% positive DC bias was added FAIL
IQaDdBqTd2A 100% positive DC bias was added FAIL

Time Chunks

Video Modification Performed Result
jHr3rSVAKMM 5 second chunk, from center of song PASS
WLathwwq3BQ 10 second chunk, from center of song PASS
9hPyB0zVZs8 15 second chunk, from center of song PASS
NzYoOzdHe5c 30 second chunk, from center of song PASS
s7gMeI2CGnw 45 second chunk, from center of song PASS
FjcspXseW7Q 60 second chunk, from center of song PASS
wcBA2ttDszg 90 second chunk, from center of song PASS
uOPmak49TZg 120 second chunk, from center of song PASS
n5peCQjsdQo 165 second chunk, from end of song PASS
oJ11oWG8tRI 30 second chunk, from start of song FAIL
z-vROygcHbY 15 second chunk, from start of song PASS
o3-HnuDI5Oo 15 second chunk, from 0:15 to 0:30 PASS

Stereo Imagery

Video Modification Performed Result
zzoR1h1oKzU Vocals were isolated FAIL
ONOTGscMQSU Vocals were removed PASS
HAd4ejov7sE Both channels were inverted FAIL
sr9mCB4uTF0 Song was knocked out-of-phase PASS
iGYzDV-8eEg Song converted to mono, then knocked out-of-phase PASS

What I Learned

… About the Content ID System

It’s everywhere: It scans every single newly-uploaded video, no matter if it has a title/description that seems suspicious. It generally finds them mere minutes after the upload completes. And videos uploaded before the system was installed aren’t immune either. It looks like it’s going through every single video that has ever been uploaded to the site, looking for copyright problems. It sounds ludicrous, but remember that YouTube is backed by Google, and Google has plenty of hardware to throw around. I have no doubt that they’ll eventually trudge through every single video, if they haven’t already finished. I wonder how much CPU time (and electricity) they squandered on this?

It’s surprisingly resilient: I really thought it would fail some of the amplification tests. Especially the +/-48 dB tests. One was so inaudibly quiet, and the other was so distorted it was completely unlistenable. It found all of them. Likewise, it could detect the sound amidst constant background noise, until the noise level passed the 45% mark. With that much noise, it overpowers the song you’re trying to hide. Likewise, it catches all subtle changes in pitch and tempo, requiring changes of up to 5% before it consistently fails to identify material.

It’s rather finicky: I can’t explain why it was able to detect the camcorder-recorded audio at 5′ and 31′, but not at 12′. Similarly, the vocal removal/isolation tests should’ve had similar results. But then again, the effectiveness of the Stereo Imagery tests depends entirely on how the song itself was engineered — Just because it turned out one way for this song, that doesn’t mean it will react the same way to the other songs with that same modification.

It’s downright dumb: Wrap your heads around this. When I muted the beginning of the song up until 0:30 (leaving the rest to play) the fingerprinter missed it. When I kept the beginning up until 0:30 and muted everything from 0:30 to the end, the fingerprinter caught it. That indicates that the content database only knows about something in the first 30 seconds of the song. As long as you cut that part off, you can theoretically use the remainder of the song without being detected. I don’t know if all samples in the content database suffer from similar weaknesses, but it’s something that merits further research.

It seems to hear in mono: When I uploaded the files with out-of-phase audio, the tests consistently passed. When the first out-of-phase test is played back in mono, the resulting audio sounds exactly like the Vocal Remove test (which also passed). When the mono-converted/out-of-phase test is played back in mono, both the channels cancel each other out and the result is (theoretically) silence. This is what the fingerprinter hears, and what it bases its conclusions on.

… About YouTube

Apparently they don’t really care about repeat infringers: I uploaded a total of 82 test videos to them, and received 35 Content ID emails. There are people out there who live in constant fear of a Content ID match, thinking that one single slip-up will get their account pulled and every single one of their videos deleted. Not so. There was a point (when I was uploading “infringing” material en masse) when I received an impressive fifteen Content ID emails in the course of an hour. Nothing happened to the account. Now, if this article becomes popular, then they might pull my test account manually… But as of the release of this article, it hasn’t happened yet.

At some point between 11/22/2008 and 1/19/2009, they changed the way they handle Content ID matches: Initially, when a video was found to be infringing a copyright, they’d immediately block access to it. You’d get an email that says “We regret to inform you that your video has been blocked from playback due to a music rights issue” and if you didn’t click the link in the email and either mute your own video or use AudioSwap on it, nobody would ever see that video again. But now things have changed… They automatically mute your videos now instead of blocking them outright. You still have the option to AudioSwap it, but the emails claim that “No action is required on your part.” They conveniently leave out the fact that they silenced the audio. And for what it’s worth, AudioSwap is fucking useless. Somebody needed to say it.

It’s very evident why they choose to mute the entire audio track of a positively ID’d video instead of just the part with the problem audio: The fingerprinter can only reliably say “yes, [one particular song] is in here, somewhere,” but it doesn’t know exactly where in the video the infringing content starts or for how long it plays. It’s far easier to just nuke the entire audio track than try to figure out precisely how to cut into it.

… About the Community

They really enjoy recordings of pure white noise: I can’t explain why people are turning to YouTube to hear this, and why it seems like my test account is the hip place to hear a flat power spectrum. But hey, three comments (almost one and a half of them intelligent) can’t be wrong.

Conclusion

It is quite possible to thwart the YouTube Content ID system, but some methods mangle the song too much to be used in anything useful.

In general, the majority of these workarounds are simply kludges which create noticeable (and often irreversible) changes in the sound of the audio. It’s likely that some of these workarounds will never be totally fixed, as the amount of computational complexity required to address some of the time/pitch changes would likely create a tremendous strain on a system that’s probably already working as hard as it can.

Reversed audio consistently gets through the fingerprinter, but that’s not very useful to human listeners. Any pitch or time alterations will also work, provided you apply a 6% or greater change to the parameter you’re adjusting.

Pure noise generators will not thwart the fingerprinter until the amount of noise overpowers the original song. Real-world noise is somewhat hit-and-miss, but the amount of effort required to introduce such noise makes the process less than worthwhile.

Stereo Imagery seems to work well, especially those modifications that make the audio play out-of-phase. Unfortunately, such audio is extremely uncomfortable to listen to in stereo mode, and it suffers from phase cancellation in mono mode, resulting in either missing vocals or total silence.

The most subtle approach is to use a resampling function, which simply increases or decreases the speed of playback. For these modifications, a speed increase of 5% or greater will work, as well as a speed reduction of 4% or greater.

A 5% Speed Increase? Are You Nuts?

You might think so. But let’s take a step back and think about what it truly means.

We base our Western ideas about music around the fact that the A note above middle C is defined as 440 Hz. Now, if we increase the speed of the song by 5%, all the pitches will shift up 5% as well. That same note becomes 462 Hz. (For reference, the A# above A440 is 466.164 Hz.) In the end, 462 Hz translates to roughly 84 cents above the A. Not quite an A#, but kinda close.

Whether or not you can hear that depends on your level of familiarity with the song. If it’s your favorite song, and you’ve committed every single note of it to memory, then yes, you’ll probably be able to tell that it seems slightly higher. But if it’s a song you’re not very familiar with, or one you haven’t heard in years, you might not be able to tell without referring to the original.

To give you a bit more perspective, consider the fact that people have been doing it for years. American films are shot at 24 frames per second. And American television runs at 30 frames per second (yes, I’m oversimplifying the hell out of this). To play film on our TVs, we simply play every 4th film frame twice and they sync up perfectly (again, gross oversimplification).

But in Europe, and the other PAL territories, the TVs run at 25 frames per second. It’s a lot harder to fit 24 film frames into 25 television frames without making an unacceptable “judder” every second. So how do they do it, then? Quite simply, they speed the 24 FPS film (and the audio) up to 25 FPS. This translates into a 4.16667% increase in speed and pitch. And I’ve never heard any Europeans complain about it. (Actually I have, but they do so in a whiny way that just makes me ignore them.)

More Forbidden Uploads

The following are 5 songs that I uploaded, knowing full well that the fingerprinter would catch them. (I’ve personally witnessed videos with these songs either muted or removed entirely.) The left column contains videos with unaltered audio tracks, all of which were detected. The videos in the right column were all resampled up by 5%.

Song Unmodified Resampled Up 5%
Bob James - Angela pjWenBsdj8o (FAIL) VZTkq1Vpnyk (PASS)
Led Zeppelin - The Lemon Song u21PJ34J_hg (FAIL) QM4YiIjNZLI (PASS)
Atreyu - Falling Down edYOcRPqdIk (FAIL) jj_eK6v5Vpw (PASS)
James Taylor - Mexico MxTlNQKqmFM (FAIL) E1tzzyV8UmU (PASS)
Yes - Tempus Fugit WXvNbs7hmI4 (FAIL) -t6S_BbybeA (PASS)

Further Reading

Disclaimer

What you do with this information is your own responsibility. I’m not here to condone or condemn the copyright laws in this country. I simply wanted to point out the flaws and idiosyncrasies in a very complex system that has become part of our modern-day culture. Always use your best judgment when deciding what types of things to upload to YouTube or any other website which uses similar technology. Don’t be an idiot, and have a nice day.

No Comments Posted Fri, 26-Jun-2009 at 10:41:59 pm in General

Music To Spend 42 Hours And 31 Minutes In A Car To

I recently embarked on a road trip with Angelo, a guy I know from RIT. He was moving out to Foster City, CA and he invited me along for the ride. It was a free cross-country trip, with one catch: I had to run the iPod, and control the music we both listened to for the duration of the journey. I decided to assemble a mix of fairly good music, with a random smattering of simply terrible songs thrown in at seemingly random intervals. If the Smitelli Radio days have taught me anything, it’s that I have a ridiculously eclectic range of musical tastes. So here, for your enjoyment, are the four On-The-Go playlists I managed to assemble for the trip.

===== 4/2/2009 =====
Jermaine Stewart - We Don’t Have To Take Our Clothes Off
Falco - Rock Me Amadeus
Fine Young Cannibals - She Drives Me Crazy
Pat Benatar - Hit Me With Your Best Shot
Cyndi Lauper - Girls Just Wanna Have Fun
Danzig - Mother
CSS - Music Is My Hot, Hot Sex
Creedence Clearwater Revival - Proud Mary
Jimmy Buffet - Cheeseburger In Paradise
Kenny Loggins - Danger Zone
Kernkraft 400 - Zombie Nation
Killers - Somebody Told Me
Led Zeppelin - Immigrant Song
Lynyrd Skynyrd - Freebird
Max Coveri - Running In The 90s
Men At Work - Down Under
Paul Engemann - Scarface (Push It To The Limit)
Night Ranger - Sister Christian
Billy Idol - White Wedding
Who - Eminence Front
The Beatles - Ob-La-Di, Ob-La-Da
The Bee Gees - Tragedy
Billy Ocean - Caribbean Queen
Blue Oyster Cult - Godzilla
Blues Traveler - Hook
Bryan Adams - Summer Of ‘69
Buster Poindexter - Hot Hot Hot
Cars - Just What I Needed
Rick Springfield - Jessie’s Girl
Right Said Fred - I’m Too Sexy
Jackson Five - I Want You Back
Jim Croce - I Got a Name
Kingsmen - Louie Louie
Kiss - Strutter
Letters To Cleo - Cruel To Be Kind
Dexy’s Midnight Runners - Come On Eileen
Dr. John - Right Place, Wrong Time
Soft Cell - Tainted Love
Steve Miller Band - The Joker
Styx - Mr. Roboto
Rush - Tom Sawyer
Rupert Holmes - Escape (Piña Colada Song)
REO Speedwagon - Can’t Fight This Feeling
.38 Special - Hold On Loosely
J. Geils Band - Centerfold
Jimi Hendrix - All Along The Watchtower
The Clash - Should I Stay Or Should I Go?
The Clash - Rock The Casbah
The Clash - London Calling
The Commodores - Brick House
Haddaway - What Is Love?
Norman Greenbaum - Spirit In The Sky
O-Zone - Dragostea Din Tei
Oasis - Wonderwall
Outfield - Your Love
Peter Gabriel - Shock The Monkey
Prozzak - Sucks To Be You
Quarterflash - Harden My Heart
Queen - Don’t Stop Me Now
Aneka - Japanese Boy
Black Sabbath - Paranoid
Blink 182 - What’s My Age Again?
Supertramp - Logical Song
Pink Floyd - Another Brick In The Wall
Huey Lewis And The News - Power Of Love
Human League - Don’t You Want Me
Iggy Pop - The Passenger
R.B. Greaves - Take A Letter, Maria
The Go-Gos - Our Lips Are Sealed
The Go-Gos - Vacation
The Go-Gos - We Got The Beat
A-Ha - Take On Me
Alphaville - Big In Japan
B-52s - Rock Lobster
Barenaked Ladies - Alcohol
Michael Jackson - Smooth Criminal
Michael Stanbello - She’s A Maniac
Murray Head - One Night In Bangkok
Phil Collins - In The Air Tonight
Sting And The Police - Message In A Bottle
Roger Troutman And Zapp - I Can Make You Dance
Debarge - Rhythm Of The Night
Dee-Lite - Groove Is In The Heart
Deep Blue Something - Breakfast at Tiffany’s
After The Fire - Der Kommissar
Del Shannon - My Little Runaway
Dire Straits - Sultans Of Swing
Dire Straits - Walk Of Life
DiVinyls - I Touch Myself
Simple Minds - Don’t You (Forget About Me)
Jethro Tull - Aqualung
Kinks - Lola
Who - Who Are You?
Wall Of Voodoo - Mexican Radio
Wang Chung - Everybody Have Fun Tonight
Cake - Italian Leather Sofa
Cheap Trick - Surrender
Caesars - Jerk It Out
Cherry Poppin’ Daddies - Zoot Suit Riot
Citizen King - Better Days
Crowded House - Don’t Dream It’s Over
Daft Punk - Harder, Better, Faster, Stronger
DiVinyls - I Touch Myself
Earth, Wind, And Fire - Let’s Groove Tonight
Eiffel 65 - Blue (Da Ba Dee)
El Mudo - Macarron Chacarron
Electric Six - Devil Nights
Everclear - AM Radio
Fixx - One Thing Leads To Another
Fleetwood Mac - Everywhere
Flobots - Handlebars
Flock Of Seagulls - I Ran
Flogging Molly - Tobacco
Bangles - Walk Like An Egyptian
Bananarama - Cruel Summer
Blessed Union Of Souls - Hey Leonardo (She Likes Me For Me)
Ziggy Marley - Give A Little Love
The Wiseguys - Start the Commotion
Will Smith - Gettin’ Jiggy With It
Toto - Hold The Line
Tommy Tutone - Jenny (867-5309)
Tom Petty - Mary Jane’s Last Dance
Derek And The Dominos - Layla
Digital Underground - The Humpty Dance
Eddie Money - Two Tickets To Paradise
Focus - Hocus Pocus
Fountains of Wayne - Hey Julie
Frank Zappa - Sexual Harassment In The Workplace
Free - All Right Now
Dazz Band - Let It Whip
Def Leppard - Pour Some Sugar On Me
Genesis - I Can’t Dance
Go West - Call Me
Golden Earring - Twilight Zone
Gorillaz - Feel Good Inc.
Mamas And The Papas - California Dreamin’
Marcy Playground - Sex and Candy
Nena - 99 Red Balloons
Dio - Holy Diver
Da Buzz - Let Me Love You Tonight
Datarock - Fa-Fa-Fa
Dave Clark Five - Catch Us If You Can
David Lee Roth - Yankee Rose
Disco Tex - Get Dancin’
The Eagles - Take It Easy
The Rolling Stones - Gimme Shelter
Men At Work - It’s A Mistake
Five Stairsteps - Ooh Child
Aerosmith - Walk This Way
Alanis Morisette - You Oughta Know
Beck - Loser
The Bee Gees - Stayin’ Alive
Ben Folds - Rockin’ The Suburbs
Bill Conti - Gonna Fly Now
Billy Preston - Outa Space
Boomtown Rats - I Dont Like Mondays
Stray Cats - Stray Cat Strut
Survivor - Eye Of The Tiger
Eve 6 - Sunset Strip Bitch
Europe - The Final Countdown
Fatboy Slim - Funk Soul Brother
Hall And Oates - Maneater
Harold Faltermeyer - Axel F (Long Version)
Jayke - Summertime Rave

===== 4/3/2009 =====
Jermaine Stewart - We Don’t Have To Take Our Clothes Off (again!)
Dispatch - Bats In The Belfry
Donovan - Mellow Yellow
Doobie Brothers - Long Train Running
Elton John - Crocodile Rock
Eric Prydz - Call On Me
Foghat - Slow Ride
Foreigner - Cold as Ice
Fountains of Wayne - Mexican Wine
Gloria Estefan - Conga
Isley Brothers - Shout
Jigsaw - Sky High
Kansas - Carry On My Wayward Son
Katrina And The Waves - Walking On Sunshine
KC And The Sunshine Band - That’s The Way (I Like It)
Madonna - Papa Don’t Preach
R.E.M. - It’s The End Of The World As We Know it (And I Feel Fine)
Ram Jam - Black Betty
Ray Parker Jr. - Ghostbusters
Rick Dees - Disco Duck
Simon And Garfunkel - Me And Julio Down By The School Yard
Sister Hazel - Change Your Mind
The Real McCoy - Another Night
Kings Of Leon - Sex On Fire
The Ting Tings - Great DJ
Tone Loc - Wild Thing
Turtles - Happy Together
Van Morrison - Wild Night
Vines - Get Free
Nerf Herder - Mr. Spock
Maze - Twilight
Maroon 5 - Makes Me Wonder
Green Day - American Idiot
Heaven And Earth - Screaming For Redemption
Inner Circle - Bad Boys
Jackson Browne - Doctor My Eyes
Jim Croce - You Don’t Mess Around with Jim
Joe Walsh - Life’s Been Good
Journey - Don’t Stop Believin’
RJD2 - Ghostwriter
Scatman John - Scatman (Ski-Ba-Bop-Ba-Dop-Bop)
Sir Mix-A-Lot - Baby Got Back
Spin Doctors - Two Princes
SR-71 - Right Now
Moby And Gwen Stefani - South Side
Modern English - Melt With You
Money Mark - Information Contraband
Moody Blues - Your Wildest Dreams
Edgar Winter - Frankenstein
Elliott Smith - Junk Bond Trader
Eurythmics - Sweet Dreams (Are Made Of This)
Bonnie Somerville - Winding Road
Aphex Twin - Windowlicker
Bryan Adams - Run To You
Buffalo Springfield - For What It’s Worth
Buggles - Video Killed The Radio Star
Delinquent Habits - Return Of The Tres
Motion City Soundtrack - A-OK
Armand Van Helden - The Funk Phenomena
MGMT - Electric Feel
Millencolin - Fox
Goldfinger - Superman
Reel Big Fish - Beer
Mighty Mighty Bosstones - The Impression That I Get
Mr. Mister - Broken Wings
Brewer And Shipley - One Toke Over The Line
Cake - Arco Arena
Strokes - Last Night
Sum 41 - The Hell Song
T. Rex - Bang A Gong (Get It On)
Thomas Dolby - She Blinded Me With Science
Thomas Rusiak - Hiphopper
The Time - Jungle Love
Tomoyasu Hotei - Battle Without Honor Or Humanity
Tower Of Power - Squib Cakes
Train - Meet Virginia
Run DMC - It’s Tricky
Screeching Weasel - Hey Suburbia
Seals And Crofts - Summer Breeze
Shiny Toy Guns - Le Disko
Uriah Heep - Easy Livin’
Urban Dance Squad - Good Grief
Stereo MC’s - Connected
3oh!3 - Don’t Trust Me
.38 Special - Trooper With An Attitude
Roy Orbison - You Got It
Rolling Stones - Paint It Black
Ride - Vapour Trail
Quiet Riot - Come On Feel The Noise
MC Hammer - U Can’t Touch This
Meatloaf - I Would Do Anything For Love
Fleetwood Mac - Peacekeeper
Nickelback - Follow You Home
Knack - My Sharona
Nirvana - Smells Like Teens Spirit
O’Jays - Love Train
Kinks - Come Dancing
NOFX - Dinosaurs Will Die
Kiss - Rock ‘N’ Roll All Night
Men Without Hats - The Safety Dance
Avalanches - Frontier Psychiatrist
ATC - Around The World
Avril Lavigne - Sk8ter Boi
L7 - Pretend We’re Dead
B-52s - Private Idaho
LFO - Every Other Time
Bell Biv DeVoe - Do Me Baby
Louden Wainwright III - Dead Skunk In The Middle Of The Road
Ben Folds - Fired
Lustra - Scotty Doesn’t Know
Billy Idol - Rebel Yell
Matisyahu - King Without A Crown
Blue Oyster Cult - Burnin’ For You
Who - I Can See For Miles
Boston - Peace Of Mind
Wolfmother - Woman
Dr. Dre - Nuthin’ But A ‘G’ Thang (With Snoop Dogg)
Dry Cell - Last Time
Echo & The Bunnymen - Lips Like Sugar
Eddie Murphy - Party All The Time
Edwin Starr - War
Byrds - Eight Miles High
Chesney Hawkes - The One And Only
Chris Isaak - Baby Did A Bad Bad Thing
CKY - Flesh Into Gear
Click Five - Just The Girl
Rick James - Superfreak
Pat Benatar - Invincible
Paul Simon - Kodachrome
Peter Gabriel - Solsbury Hill
Aqua - Lollipop (Candyman)
Bobby Hebb - Sunny
Booker T. And The MG’s - Green Onions
Elizabeth Daily - Shake It Up
Elwood - Sundown NG Remix
Enur - Calabria 2007
Everclear - I Will Buy You A New Life
Gorillaz - 19-2000
Irene Cara - Flashdance (What A Feeling)
John Fogerty - The Old Man Down The Road
Rolling Stones - Beast Of Burden
Romantics - What I Like About You
Roger Troutman And Zapp - More Bounce To The Ounce
Eagles - Victim Of Love
Electric Six - Danger! High Voltage
Eric Clapton - I Shot The Sheriff
Sting And The Police - Roxanne

===== 4/4/2009 =====
Jermaine Stewart - We Don’t Have To Take Our Clothes Off (I’m sensing a pattern here)
Stevie Nicks - Edge Of Seventeen
Sublime - Santeria
Sugarloaf - Green-Eyed Lady
Tito Puente - Mambo Gallego (D-Code Remix)
Toad The Wet Sprocket - Something’s Always Wrong
Tom Petty And The Heartbreakers - Refugee
Tom-Tom Club - Genius Of Love (Extended Version)
Tommy Seebach - Apache
War - Why Can’t We Be Friends
Weezer - El Scorcho
Wheatus - Teenage Dirtbag
Whispers - And The Beat Goes On
Wild Cherry - Play That Funky Music
Yello - Oh Yeah
ZZ Top - Tush
Zutons - Pressure Point
Gary Numan - Cars
George Thorogood - Bad To The Bone
Gordon Lightfoot - Carefree Highway
Rolling Stones - Shattered
Rufio - White Lights
Shakira - Whenever, Wherever
Third Eye Blind - Semi-Charmed Life
Loverboy - Working For The Weekend
Lynyrd Skynyrd - That Smell
Frida - I Know There’s Something Going On
Gerardo - Rico Suave
Van Stephenson - Modern Day Delilah
AC/DC - You Shook Me All Night Long
Aerosmith - Janie’s Got A Gun
Alarm - 68 Guns
Billy Idol - Cradle Of Love
Carole King - I Feel The Earth Move
Warren Zevon - Werewolves Of London
Weezer - Buddy Holly
Yaz - Situation
Carly Simon - You’re So Vain
Ok Go - You’re So Damn Hot
Pink - Get The Party Started
Pinback - Good to Sea
Phunk Junkeez - I Love It Loud
Red Hot Chili Peppers - Give It Away Now
Rick Astley - Never Gonna Give You Up
Lemon Demon - The Ultimate Showdown Of Ultimate Destiny
Lenny Kravitz - I Want To Get Away
Led Zeppelin - Dy’er Ma’ker
Los Lonely Boys - Señorita
Matchbox Twenty - Disease
Modest Mouse - Float On
Monkees - Daydream Believer
Natalie Imbruglia - Torn
Nelly Furtado - Turn Off The Light
Chuck Mangione - Feels So Good
Chumbawamba - Tubthumping
Compton’s Most Wanted - Hood Took Me Under
Cranberries - Zombie
Gipsy Kings - Hotel California
Alanis Morissette - Head Over Feet
Fastball - Out of My Head
Crazy Town - Butterfly
Blink 182 - All The Small Things
TLC - No Scrubs
MGMT - Time to Pretend
Katy Perry - Hot N Cold
Ida Maria - I Like You So Much Better When You’re Naked
The Asteroids Galaxy Tour - Around The Bend
Interpol - The Heinrich Maneuver
Mindless Self Indulgence - Straight To Video
Timbuk 3 - The Future’s So Bright, I Gotta Wear Shades
Weird Al Yankovic - Frank’s 2000″ TV
Beach Boys - Kokomo
The Beatles - Come Together
Bee Gees - This Is Where I Came In
Bert Kaempfert - Afrikaan Beat
Billy Joel - We Didn’t Start The Fire
Black Crowes - Hard To Handle
Black Eyed Peas - Let’s Get It Started
Blondie - One Way Or Another
Bond - Ride
Box Tops - The Letter
Eddie Rabbit - I Love A Rainy Night
Edie Brickell - What I Am
Elton John - Levon
Band - Up On Cripple Creek
Heart - Barracuda
Kraftwerk - Popcorn
Laura Branigan - Self Control
Letters To Cleo - Co-Pilot
Wham! - Wake Me Up Before You Go-Go
Reel Big Fish - Hungry Like The Wolf
Reckless Kelly - Nobody’s Girl
Presidents Of The United States Of America - Lump
Lionel Richie - Running With The Night
Kenny Rogers - Just Dropped In (To See What Condition My Condition Was In)
John Stewart - Gold
John Fogerty - Centerfield
Joe Esposito - You’re The Best
Jimi Hendrix - Bold As Love
Joan Jett - I Love Rock And Roll
Kansas - Dust In The Wind
New Edition - Candy Girl
Nickelback - Photograph
Nitty Gritty Dirt Band - Mr. Bojangles
No Connection - Living American
No Doubt - Just a Girl
Blue Swede - Hooked On A Feeling
Seether - Fine Again
Spencer Davis Group - Gimme Some Lovin’
Bruce Hornsby - The Way It Is
Crossfade - Dead Skin
Culture Club - Do You Really Want To Hurt Me
Dave Matthews Band - So Right
Disturbed - Down With The Sickness
Dobie Gray - Drift Away
Eagles - Hotel California
Eddie Money - Take Me Home Tonight
EMF - Unbelievable
Eve 6 - On The Roof Again
Fabulous Thunderbirds - Tuff Enuff
Simon And Garfunkel - Mrs. Robinson
Serena Paris - Look At Us Now
Scientist - Ghost Of Frankenstein
Santana - Forget About It (With Rob Thomas)
Hampton The Hamster - The Official Hamster Dance
Hamilton, Joe Frank And Reynolds - Don’t Pull Your Love
Herbie Hancock - Rockit
Ides Of March - Vehicle
Bryan Adams - Cuts Like A Knife
Brownsville Station - Smokin’ In The Boys Room
Boz Scaggs - Lowdown
Creedence Clearwater Revival - Fortunate Son
BJ Thomas - Raindrops Keep Falling On My Head
Black Sabbath - Iron Man
Camper Van Beethoven - Take The Skinheads Bowling
Canned Heat - Goin’ Up The Country
Chakachas - Jungle Fever
Cheap Trick - Dream Police
Contraband - Tonight You’re Mine
Big Country - In A Big Country
Blues Traveler - But Anyway
Blümchen - Heute Ist Mein Tag
Bob James - Nautilus
Cake - Mr. Mastodon Farm
Cypress Hill - Insane In The Membrane
Daft Punk - One More Time
Dire Straits - So Far Away
Earth, Wind, And Fire - September
Edison Lighthouse - Love Grows (Where My Rosemary Goes)
Electric Light Orchestra - Four Little Diamonds
Everlast - What It’s Like
Everclear - Slide
Panic! At The Disco - Lying Is The Most Fun A Girl Can Have Without Taking Her Clothes Off
Papa Roach - Last Resort
Pat Benatar - Love Is A Battlefield

===== 4/5/2009 =====
Jermaine Stewart - We Don’t Have To Take Our Clothes Off (Four for four!)
Peter Gabriel - More Than This
Trashmen - Surfin’ Bird
Don Henley - Boys Of Summer
Doobie Brothers - Listen To The Music
Paul Mauriat - Love Is Blue
Mason Williams - Classical Gas
Nina Gordon - Tonight And The Rest Of My Life
Matchbox 20 - Push
Semisonic - Closing Time
Shaggy - It Wasn’t Me
Boston - Rock And Roll Band
Bowling For Soup - 1985
Reel Big Fish - Take On Me
Rolf Harris - Tie Me Kangaroo Down, Sport
Roxette - She’s Got The Look
Rush - Subdivisions
Sam The Sham And The Pharaohs - Lil’ Red Riding Hood
Scorpions - Rock You Like A Hurricane
Simple Plan - I’d Do Anything
Diffuser - Get It On
ABBA - Dancing Queen
Ace Of Base - All That She Wants
Alien Ant Farm - Glow
Amy Holland - She’s On Fire
Animals - House Of The Rising Sun
Bachman-Turner Overdrive - Takin’ Care Of Business
Bad Religion - I Love My Computer
Barenaked Ladies - One Week
Bill Withers - Lean On Me
Billy Idol - Mony Mony
Billy Joel - For The Longest Time
Billy Ocean - When The Going Gets Tough
Bloodhound Gang - Jackass
Blue Oyster Cult - In Thee
Boston - Foreplay (Long Time)
Brother Louie - Stories
Brothers Johnson - Strawberry Letter 23
Cars - My Best Friend’s Girl
Centurians - Bullwinkle Part II
Chiffons - One Fine Day
Cornelius Brothers - Too Late To Turn Back Now
Corrs - Leave Me Breathless
Counting Crows - Big Yellow Taxi
Pure Prairie League - Let Me Love You Tonight
Rage Against The Machine - Guerilla Radio
Rembrandts - I’ll Be There For You
Whitesnake - Here I Go Again
Cake - I Will Survive
Contours - Do You Love Me (Now That I Can Dance)
Cowsills - Hair
Cream - White Room
Creedence Clearwater Revival - Heard It Through The Grapevine
Daddy Dewdrop - Chick-A-Boom (Don’t Ya Jes’ Love It)
Darren Hayes - Insatiable
Death Cab For Cutie - Soul Meets Body
Deep Purple - Highway Star
Dire Straits - Money For Nothing
DJ Tomcraft - Overdose
Doobie Brothers - Black Water
Doors - Hello, I Love You
Dusty Springfield - Son Of A Preacher Man
Eagles - Already Gone
Ernies - Here And Now
Europe - Carrie
Everclear - Overwhelming
Exile - Kiss You All Over
Faith No More - Midlife Crisis
Ferrante And Teicher - Exodus
Five For Fighting - 100 Years
Five Man Electrical Band - Signs
Fleetwood Mac - Rhiannon
Foghat - Fool For The City
Free - All Right Now
Frou Frou - Must Be Dreaming
Fugees - No Woman, No Cry
James Gang - Funk #49
Jane’s Addiction - Been Caught Stealing
Jefferson Airplane - Somebody To Love
Starship - We Built This City
Starland Vocal Band - Afternoon Delight
Steam - Na Na Hey Hey Kiss Him Goodbye
Steely Dan - Do It Again
Steve Miller Band - Take the money and run
Steve Winwood - Higher Love
Tavares - Heaven Must Be Missing An Angel
Toasters - Two Tone Army
Tom Jones - It’s Not Unusual
Scientist - Your Teeth In My Neck
Searchers - Love Potion #9
Seatbelts - Tank!
Who - You Better, You Bet
Plimsouls - A Million Miles Away
Pointer Sisters - I’m So Excited
Warrant - Cherry Pie
White Stripes - Fell In Love With A Girl
Ventures - Hawaii Five-O
Petula Clark - Downtown
Phil Collins - Sussudio
Poison - Every Rose Has Its Thorn
Presidents Of The United States Of America - Peaches
Proclaimers - Over And Done With
Rammstein - Amerika
Rascals - Good Lovin’
Red Hot Chili Peppers - Give It Away Now
Rippingtons - Welcome To The St. James Club
Robert Palmer - Some Like It Hot (The Power Station)
Rush - Spirit Of The Radio
Saliva - Click Click Boom
Sam Cooke - Chain Gang
Sammy Hagar - Your Love Is Driving Me Crazy
Santana - Oye Como Va
Shawn Mullins - Lullaby
Sheena Easton - Morning Train
Shirelles - Mama Said There’d Be Days Like This
Simon And Garfunkel - A Hazy Shade Of Winter
Smash Mouth - Then The Morning Comes
Smashing Pumpkins - Bullet With Butterfly Wings
The Smiths - How Soon Is Now?

Also, Angelo took it upon himself to log our GPS coordinates for the majority of the trip. He’s working on putting together some graphs and maps, but I had a better idea of what to do with the data. I hacked together a script that converts the list of coordinates into Google Street View panoramas, and then I played them in a time-lapse video. It’s pretty damn cool, if I do say so myself: http://www.youtube.com/watch?v=b2d-VAAyB9c. If there’s enough interest, I’ll explain how I did it in a later post.

No Comments Posted Thu, 11-Jun-2009 at 3:59:40 pm in General

You came and you gave without taking. Like a BitTorrent seed.

“The Breakfast Club soundtrack. This should clear the room after the reception.” –Yancy Fry, Jr.

There is an important and often overlooked skill that every DJ should possess: The ability to play a song at the end of an event that will succeed in driving everybody out of the venue. Let’s face it, there are always a few stragglers who never seem to want to leave, despite the fact that practically everybody else is long gone. They haven’t picked up on the subtle hints, so perhaps its time to blast some “Achy Breaky Heart” to send them on their way. Or how about “MacArthur Park,” “Seasons in the Sun,” or (god help us) “The Christmas Shoes?”

Might I also suggest “Mandy” by Barry Manilow? Yes, this 1974 dirge lumbers along at a whopping 104 BPM for almost three and a half minutes. Driven mainly by a piano, with a string section during the choruses and a drum kit entering and exiting, this track isn’t exactly a toe-tapper. But I doubt anybody would expect anything different for a song about a man’s love-induced delirium tremens (”You kissed me and stopped me from shaking”).

So why do I bring up this song in particular, when there are probably thousands of other songs that feel too slow and drone too long? Because I have a background in drumming, and every time I hear this song played, it honestly feels like it’s speeding up. It feels like the drummer (Jim Young, as far as I can research) wanted nothing more than for the song to be over as quickly as possible. It’s almost as if nobody wants to sit and listen to this thing, including the people that made it!

Whenever I try to bring this point to anybody’s attention, they never seem to grasp what I’m saying. Either they’ve never heard the song, or they lack the sense of rhythm to feel the tempo start to drift. During my last attempt to explain this phenomenon, I facetiously asked, “Do I need to make a graph to explain this to you?”

I realized at that precise moment, yes. Yes I did.

I begrudgingly opened the file in Adobe Audition and began the thrilling process of placing a cue marker at the exact downbeat of every bar in the song. There were 87 of them that I could hear. (I probably could’ve estimated the 88th, but the fade-out cut it short and I didn’t want to muddy my “research” with a guess like that.) Do you have ANY idea what it’s like to have to scrub 87 different sections of this song trying to find every downbeat? It was painful. But I had to do something while waiting for the cable guy to come and (fail to) unfutz the tap in the basement…

87 points
(A shadow of a man, a face through a window, crying in the night.)

It’s a difficult task finding the exact location of each downbeat in a song like this. Generally you’d go with the drums, as there’s almost always a kick at each point you want. But not so much here; I had to rely on the bass and in some cases the piano to locate these. I never used the vocals, as they aren’t rigidly timed in this song. All in all, I’d say all of these points are accurate to 50 ms or less, which is pretty good considering how ridiculous this whole project is.

Once all the cues were placed and lined up, I merged them all together. This converts each cue from a simple point in time into a range of time - the cue starts at the point where it was originally placed, and extends right up to the cue directly after it. That cue in turn extends out to the cue that follows it, and so on until the end of the file. So cue 23 would correspond to the entire 23rd bar of the song (”… never realized / How …”), while cue 24 would contain the entire contents of the next bar (”… happy you made me, oh Ma- …”).

Since the cues on the file are set up to perfectly mirror the bars in the song, we can use Audition’s Marker window to see how long each cue is, and by extension, how long each bar is. The longest cue in the song is actually the very first bar of the first verse (bar 9), right after the piano intro. If you listen to the song, you can hear it drag inexplicably as he sings “I remember all my life”. Conversely, the shortest bar is 80, during the very last line of the song (”And I need yoooooooou”). These bars are 2524 ms and 2134 ms long, respectively. Converting them into beats per minute, we get 95.1 BPM and 112.5 BPM, with the remainder of the song somewhere in between the two. The song’s overall average tempo is 104.6 BPM (calculated from 87 bars in 199.53 seconds). That’s a pretty wide swing, by most standards.

Because I’m a masochist, I threw the raw bar length data into an Excel spreadsheet to see if I could pick an overall trend out of it.

Mandy, Bar-by-Bar Graph
(Caught up in a world of uphill climbing.)

The X-axis is divided up into bars. As you read left-to-right, the song plays normally in time. The Y-axis indicates the length of each measure of the song, with higher numbers indicating longer amounts of time, and therefore slower tempo. The points and the lines are color-coded to make it somewhat easier to tell what part of the song you’re at.

Maroon - 8-Bar Piano Intro
Orange - 16-Bar Verse (”I remember all my life”)
Tan - 8-Bar Chorus
Maroon - 4-Bar Piano Interlude
Orange - 8-Bar Verse (”I’m standing on the edge of time”)
Tan - 8-Bar Chorus
Pink - 6-Bar Horn Solo
Teal - 4-Bar Bridge (”Yesterday’s a dream”)
Tan - 16-Bar Chorus (8-bar chorus played twice)
Green - Lead-Out

There is a squiggly vertical line between bars 70 and 71; this is where the chorus repeats itself, and also when the song modulates up into a higher key. Not that it matters terribly, but I just wanted to include it to maybe help you follow along at home.

Finally, each section has a horizontal blue line. That corresponds to the average of all the points in that particular section of the song. Going by the section averages alone, the song shows a very odd slope, initially slowing down until the first chorus, then speeding back up as the song goes on. (Remember, shorter lengths mean faster tempos.)

What conclusions can I draw from this? Well, first and foremost, I need a life. Secondly, this song definitely slows down until the first chorus or so, at which point it speeds up until it ends. Anything beyond that is left as an exercise for the reader.

… WHAT WAS I THINKING?

2 Comments Posted Thu, 24-Jul-2008 at 3:24:50 am in The Armchair Audio Engineer

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