So basically, labels are now treating music production as an optimization problem.
And because they're terrible at this type of optimization problem, they're getting stuck in a local minimum (the space is anything but convex), which happens to be repetitive, awful, and targeted at the largest common denominator.
Treating music as an optimization problem may not be a bad thing in itself, the issue is being unable to solve the problem properly.
Are they really terrible at it? You seem to assume that the global minimum to the problem is an artistically perfect song. For record labels the global minimum is the song that makes the most money. If that song happens to be repetitive and awful, so be it.
Article makes it seem like Shazam has metrics like a social network, but my experience, and everyone I've ever seen is that it's rarely used -- and simply to quickly identify a song.
This latest one in The Atlantic implies people use it far more frequently then I think they do, and I'm again, very skeptical. And even if they do, they don't shazam hits they know, or songs about to be hits, but they shazam that random song they can't identify -- not sure that's an indicator of hit'ness.
From user numbers, to capital raised, to stories like this, each time I read about Shazam, I say to myself "Gosh, I really love the app, but can it really be this big and lucrative?" I feel like I'm missing something.
Think about the kind of data that they would collect.
I personally rarely use Shazam, but when I do, it's more often then not a song I don't know at all rather than trying to remember a song's name.
So someone like me hears a song and wants to identify it. Then another person, and another. So you have three people in a specific location at the same time looking to identify a song. That would suggest that those people like what they hear. You know what the song is so you know interest has piqued. If you cross reference that with radio plays at that time you have a new metric that you couldn't measure previously - reaction to a radio play. It's incredibly interesting because of the type of data it collects, which to be honest I think the article explains explicitly and very clearly!
How is this surprising? Shazam tracks searches and location. If a lot of people search a specific song in a specific location, that means they 1) liked the song, but it's new to them 2) the song is nevertheless played quite often. Both of these are good predictors for a viral hit. Because nobody uses Shazam on well-known songs, this filters out a lot of noise.
>, they don't shazam hits they know, or songs about to be hits, but they shazam that random song they can't identify -- not sure that's an indicator of hit'ness.
One of the Shazam folks gave a 10 minute presentation at O'Reilly conference.[1] She gave an example of how early Shazam datapoints correctly predicted that Katy Perry's song would beat Lady Gaga's song. (The singles were released at the same time.)
Perhaps she was cherry picking clear correlations but it doesn't seem too far fetched that the early trends it sees from users is a reliable prediction of a future Billboard chart position and sales.
I use Shazam as a kind of "music bookmarking" service. I hear a track I don't know, and I Shazam it to identify the track and it's saved into the list for later. That's pretty valuable data for Shazam.
See "An Industrial-Strength Audio Search Algorithm"[1], a 2010 paper written by one of the Shazam developers. The analysis itself doesn't seem complicated[2] but having a large enough database to compare against is probably the hard part.
No idea but I'd guess instead of simply comparing the received signal with an internal database of time domain signals, one's database is split into different categories (according to certain features). So you could then quickly go down a tree of options until you end up a song with the most similar ones (features). The unique combination of features would make up the "acoustic fingerprint" they're talking about.
That's probably too obvious though I'd imagine there's something else going on as well.
From memory, they do a spectrum analysis (frequency vs time) and look for peaks, specifically sets of three peaks in a row, evenly spaced. Gather a lot of them and send them in.
I would imagine that it's good because it doesn't require any more information that what a song sounds like. A user hears a song they like, records it there and then and that shows 'interest'. Twitter on the other hand needs a name / artist.. you can't really analyse a tweet saying "I like that song that goes du du dud dud dahhh"... but Shazam actually can.
I didn't see a problem with it. The magazine is "The Atlantic" which has a general readership.
If one writes a program to analyze Shakespeare text, he might describe it as "converted the text into a multi-dimensional vector space" or "mapped ngrams into a point cloud" -- and that would be acceptable lexicon for a Dr Dobbs Programming Journal or possibly ArsTechnica. However, for The Atlantic, it seems reasonble for a journalist/editor to write that as "convert the plays into data."
Yes, Shakespeare the text itself was already "data" so in a pedantic analysis, saying "converting data to data" seems pointless but that's not the level of indirection the general readership is at.
I LOATHE NOTIFICATIONS that are junk! Far too many apps do this. I turn them all off now. This is why we can't have nice things - appdevs cramming too much stuff into stupid notifications.
Years ago there was a GNKSA - Good Net Keeping Seal of Approval. This was a set of criteria for Usenet clients to promote sensible usability and formatting standards.
There's probably a niche for something similar for apps.
[+] [-] nirkalimi|10 years ago|reply
Great article regardless. Glad to see more peoples take on it.
[+] [-] fchollet|10 years ago|reply
And because they're terrible at this type of optimization problem, they're getting stuck in a local minimum (the space is anything but convex), which happens to be repetitive, awful, and targeted at the largest common denominator.
Treating music as an optimization problem may not be a bad thing in itself, the issue is being unable to solve the problem properly.
[+] [-] wmkn|10 years ago|reply
[+] [-] davidu|10 years ago|reply
Example: http://www.forbes.com/sites/parmyolson/2014/08/20/shazam-hit...
Article makes it seem like Shazam has metrics like a social network, but my experience, and everyone I've ever seen is that it's rarely used -- and simply to quickly identify a song.
This latest one in The Atlantic implies people use it far more frequently then I think they do, and I'm again, very skeptical. And even if they do, they don't shazam hits they know, or songs about to be hits, but they shazam that random song they can't identify -- not sure that's an indicator of hit'ness.
From user numbers, to capital raised, to stories like this, each time I read about Shazam, I say to myself "Gosh, I really love the app, but can it really be this big and lucrative?" I feel like I'm missing something.
I hope I'm wrong.
[+] [-] mintone|10 years ago|reply
I personally rarely use Shazam, but when I do, it's more often then not a song I don't know at all rather than trying to remember a song's name.
So someone like me hears a song and wants to identify it. Then another person, and another. So you have three people in a specific location at the same time looking to identify a song. That would suggest that those people like what they hear. You know what the song is so you know interest has piqued. If you cross reference that with radio plays at that time you have a new metric that you couldn't measure previously - reaction to a radio play. It's incredibly interesting because of the type of data it collects, which to be honest I think the article explains explicitly and very clearly!
[+] [-] Grue3|10 years ago|reply
[+] [-] jasode|10 years ago|reply
One of the Shazam folks gave a 10 minute presentation at O'Reilly conference.[1] She gave an example of how early Shazam datapoints correctly predicted that Katy Perry's song would beat Lady Gaga's song. (The singles were released at the same time.)
Perhaps she was cherry picking clear correlations but it doesn't seem too far fetched that the early trends it sees from users is a reliable prediction of a future Billboard chart position and sales.
[1]https://www.youtube.com/watch?v=mcTPvxo8SXY
deep link at 3:32 that compares KP and LG:
https://youtu.be/mcTPvxo8SXY?t=3m31s
[+] [-] girvo|10 years ago|reply
[+] [-] gcb0|10 years ago|reply
they have 2min ads on feature films showing music news.
i bet this is but paid PR. they are desperate.
the music id business is so dead. Google, apple, yahoo... everyone does it!
[+] [-] geographomics|10 years ago|reply
[+] [-] bsder|10 years ago|reply
[+] [-] unicornporn|10 years ago|reply
[+] [-] unknown|10 years ago|reply
[deleted]
[+] [-] spatten|10 years ago|reply
That doesn't sound right. On average one identification for every 16 downloads? I'm guessing that second million should be a billion.
[+] [-] idlewords|10 years ago|reply
[+] [-] lumberjack|10 years ago|reply
I'm currently in the planning stage for a similar project and this might end up being really helpful for me.
EDIT: Thanks guys.
[+] [-] anonova|10 years ago|reply
[1]: http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf
[2]: http://www.royvanrijn.com/blog/2010/06/creating-shazam-in-ja...
[+] [-] Nimitz14|10 years ago|reply
That's probably too obvious though I'd imagine there's something else going on as well.
[+] [-] pronoiac|10 years ago|reply
[+] [-] aditya|10 years ago|reply
1. http://developer.echonest.com/forums/thread/3650
[+] [-] cpeterso|10 years ago|reply
[+] [-] mkagenius|10 years ago|reply
[+] [-] mintone|10 years ago|reply
[+] [-] golemotron|10 years ago|reply
I cringe and then laugh inside whenever I see tech writing like this.
[+] [-] jasode|10 years ago|reply
If one writes a program to analyze Shakespeare text, he might describe it as "converted the text into a multi-dimensional vector space" or "mapped ngrams into a point cloud" -- and that would be acceptable lexicon for a Dr Dobbs Programming Journal or possibly ArsTechnica. However, for The Atlantic, it seems reasonble for a journalist/editor to write that as "convert the plays into data."
Yes, Shakespeare the text itself was already "data" so in a pedantic analysis, saying "converting data to data" seems pointless but that's not the level of indirection the general readership is at.
[+] [-] eghri|10 years ago|reply
[+] [-] dghughes|10 years ago|reply
I'm on the cusp of deleting Shazam as useful as it is it's getting more spammy as the days go by too many notifications of junk.
[+] [-] pervycreeper|10 years ago|reply
Context? What did they do?
[+] [-] DanBC|10 years ago|reply
I LOATHE NOTIFICATIONS that are junk! Far too many apps do this. I turn them all off now. This is why we can't have nice things - appdevs cramming too much stuff into stupid notifications.
Years ago there was a GNKSA - Good Net Keeping Seal of Approval. This was a set of criteria for Usenet clients to promote sensible usability and formatting standards.
There's probably a niche for something similar for apps.
https://en.wikipedia.org/wiki/Good_Netkeeping_Seal_of_Approv...