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This was the smart approach when Shazam launched in 2008. I would have done exactly the same thing - gone straight to developing a method to turn every song int
by mmaunder 3y ago
This was the smart approach when Shazam launched in 2008. I would have done exactly the same thing - gone straight to developing a method to turn every song into a hash as computationally efficiently as possible. If you launched this today the default R&D approach would be to train a model which may turn out to be far less efficient and more expensive to host. It feels like the kind of thing a model might be good at, but given that there are a finite number of songs, taking a hash-based approach is probably way more performant.
- AntonioCao 3y agotbh for tools like Shazam there's no fundamental difference between a database + hashing algorithm and a self-supervised model; both are great indexing & compression solutions, just for different scales of data.
- deleted 3y ago[deleted]
- mattl 3y ago2008? I remember using it in the UK in 2000/2001?
- wruza 3y agoWas it iphone or android?
- tomduncalf 3y agoIt was a phone number you called, which then SMSed you the result
- bloqs 3y agoIt was originally sms service
- aidenn0 3y agoPretty sure it launched in 2002 for 60p per song.
- guyomes 3y agoThe smart approach in 1975 was to use Parsons code, which was also turning songs into hashes, computable in your head. You could then find your song back as simply as looking a word in a dictionary. Hopefully this idea won't die any time soon. [1]: https://en.wikipedia.org/wiki/Parsons_code https://en.wikipedia.org/wiki/Parsons_code
- aidenn0 3y agoThat requires identifying the melody, which is certainly not something all humans can do, and was probably not generally doable by a machine in 1975. It also throws away a huge amount of information, and requires starting from the beginning of the melody.
- huhtenberg 3y agoI'd guess that this won't work well for EDM (electronic dance music) tracks.
- aidenn0 3y ago> This was the smart approach when Shazam launched in 2008. Nitpick, but Shazam launched in 2002 as a dial-in service that replied with a text-message of the result. The first phone app was for BREW in 2006. The 2008 date is just when Apple launched the app store; it was not possible for a third party to make an iPhone app before 2008.
- justusthane 3y agoThat’s more than a nitpick, that’s incredible! It still feels a bit magic today; I can’t imagine how magic that seemed in 2002!
- Symbiote 3y agoIt launched in Britain. I remember dialing 2580 in a nightclub, waving the phone around, and everyone being impressed when the identification was correct, as it usually was. Even with relatively obscure music.
- CiaranMcNulty 3y agoA friend was in the beta and demoed it to us in a bar.. it was insane In the UK you dialled 2580 from your (non smart) cellphone, it would hang up after a few seconds and you’d get an SMS right away with the ID of the track
- DeathArrow 3y agoWell, under the hood a neural net kind of also builds hashes, just less accurate.
- ixbt77 3y agoThis is true, they even tried to treat it as a speech recognition (ASR) problem in 2012: https://research.google/pubs/pub37754/ https://research.google/pubs/pub37754/
- simonw 3y agoIf you trained a model for this, how would you avoid having to run the entire training process again every time you needed to add another song? I wonder if there's a way to build an embeddings model for this kind of thing, such that you can calculate an embedding vector for each new song without needing to fully retrain.
- koolba 3y agoIf the model is successful, it will be able to predict the artist and song title for music it’s never heard.
- zzzcsgo 3y ago[dead]
- zer00eyz 3y agoNo, People who are highly skilled at this, can be easily stumped. Sure it might workfor artist who are more focused (tailor swift), it might pick out some interesting guest appearances (Eddie Van Halen on Beat It) but when you get multi talented performers who change everything about what do, they don't fit a "model". The most current example would be Andre3000's latest release.
- ravetcofx 3y agoGiven our current trajectory this will probably be possible in 10 to 15 years
- anon84873628 3y agoUm, yeah, you won't be able to model artists who don't follow a model (especially done so deliberately). As you say that is true of humans or computers alike. But it's not the problem anyone cares about and not what the parent comment intended. Certainly a well trained model will be able to have incredible accuracy just with vocals alone. It will be able to identify Lady Gaga regardless of whether she is singing a new art pop track or old standard with Tony Bennett.
- FirmwareBurner 3y ago>This was the smart approach when Shazam launched in 2008. A noteworthy mention would be that Sony's TrackID did most likely the same thing on their feature phones a few years before Shazam.
- deleted 3y ago[deleted]
- bartonsurfer1 3y agoHi - This is Chris Barton (founder of Shazam). Sony's TrackID was built by licensing (and later buying) a technology invented by Philips. That tech was invented after Shazam. Shazam was the first to create an algorithm that identifies recorded music with background noise in a highly scaled fashion. My co-founder, Avery Wang, invented the algorithm in 2000. Chris (www.chrisjbarton.com)
- loxias 3y agoCough Cough, Geoff Schmidt, Matthew Belmonte, Tuneprint? Edit: tho for sure, the Philips algorithm was better than either of ours.
- aidenn0 3y agoTuneprint was published in 2004, no? Shazam filed their patents in 2000 and launched in 2002.
- loxias 3y agoNo no no, Tuneprint was well before that. By 2004 we were LONG gone. Shazam didn't show up until I think years later. And I might be confusing them with another group but I thought, at the time, they were doing some goofy hash of the highest energy Fourier components -- a source of entertainment in our office. ;-) I think Geoff had the vision and algorithm from the 90s as part of an ISEF project (!?). We had funding in 2001, when we got the real world go-to-your-car-and-get-a-cd and then we identify it ... using the audio signal alone.... demo working. With a corpus of hundreds of thousands of songs. Positive match in less than 2 seconds. Sadly, in 2001 there's no market for such whizbang amazing tech.
- crazygringo 3y ago> to turn every song into a hash Just to be clear, it's not turning each song into a hash. It's turning each song into many hundreds (thousands?) of hashes. And then you're looking for the greatest number of mostly-consecutive matches of tens (or low hundreds) of hashes from your shorter sample. Also, I don't think this would be done with training a model today, because you're adding many, many new songs each day, that would necessitate constant retraining. Hashes are still going to be the superior approach, not just for efficiency but for robustness generally.
- brainfog 3y ago> that would necessitate constant retraining You wouldn't necessarily need to retrain that frequently. If your model outputs hashes / vectors that can be used for searching, you just need to run inference on your new data as it comes in.
- Legend2440 3y agoDefinitely this, embeddings would be the modern approach.
- raylad 3y agoThe "modern" approach sounds like it would be a lot worse than this approach both in terms of input and runtime performance. Trendy ("modern") is not necessarily better.
- casualscience 3y agoI'm an MLE, I would probably chop the songs into short segments, add noise (particularly trying to layer in people talking, room noise, and apply frequency-based filtering), and create a dataset like that. Then I would create contrastive embeddings with a hinge loss with a convnet on the spectrogram. Ultimately this looks the same, but the "hashes" come from a convnet now. But you still are doing some nearest neighbor thing to actually choose the best match. I imagine this is what 90% of MLEs would do, not sure if it would work better or worse than what Shazaam did. Prior to knowing Shazaam works, I might think this is a pretty hard problem, knowing Shazaam works, I am very confident the approach above would be competitive.
- dylan604 3y agoI could totally see RIAA shutting it down or ASCAP issuing a licensing invoice to stop a train on its tracks just because that's how they think
- a2dam 3y agoWhat makes you say this? They existed and there were a ton of similar products as well. None of them got any grief from those organizations.
- dylan604 3y agonone of the companies making the previous hashing apps had the money that AI companies do. around here, the phrase "fish in a barrel" might be used to describe the situation.