Show HN: Get the guitar chords from your Spotify playlists (opens in new tab)

If anyone knows of any apps (even prototypes) that can do this, please provide links.

I mean, computational complexity aside it seems like at least hypothetically you could even just apply basic auto-correlation-style logic to detect the period of the combined wave much like you do in the monophonic case (assuming the chord is sustained for long enough to actually capture that full period, which of course it won't be in the general case). There's nothing magical about a neural-net or other deep-learning-style solution to this problem - at the end of the day that's just an approximation of a formula that could in theory be derived through more direct means anyway. And (as far as I know) there's no reason to believe the polyphonic case is fundamentally resistant to more traditional techniques.

And as implied by your comment, the problem is made easier (or at least less resource-intensive) in practice than it is in the abstract: we're mostly interested in audio that's comprised of actual notes from the chromatic scale (rather than a combination of arbitrary frequencies). There's only ~140 or so component frequencies we really need to consider in practice. (Not to mention the semi-predictable repetition/progression patterns you're likely to encounter in most conventional songs. That's inadequate by itself but certainly a good way to error correct, fill in gaps, resolve ambiguous cases, etc.)

But that said, it does seem like polyphonic pitch detection is a problem that responds really well to machine-learning techniques. In my experience, even a fairly simplistic ANN (e.g., no hidden layers, ~1k to ~10k weights depending upon how the inputs/outputs are modeled) - when seeded with a little bit of domain-specific knowledge - can very quickly learn to perform reliable polyphonic pitch detection under real-world conditions.

To be fair, I haven't quite put my money where my mouth is on this topic (yet): I develop software that includes this sort of functionality and the current production version uses more conventional (or at least direct) analysis rather than so called "deep learning" techniques for polyphonic pitch detection. There are pros and cons to either approach, but I can definitely see why some find the deep learning solution attractive. There's probably some degree of magical thinking involved (i.e., "AI will solve this pattern recognition problem that's too hard for me to work out from first principles"), but it also seems to work really well in this case.

For what it's worth I think you've got the right general idea, or at least (based on your brief description) I think I arrived at a solution that's based on some similar concepts and found it fairly effective (beyond the proof-of-concept phase). And as you noted there are related concepts discussed in some of the published academic research. I'd love to hear a little more about your approach if you're willing and able to share any more details. (Noting that at least part of my interest in that topic is selfish, of course.)

Someone above mentioned: "Here comes the sun", which is a great example.

First gotcha, it sort of sounds finger picked, but it's not it's flat picked. Another unusual thing about that song is that it's played w/ a capo on 7th fret.

Now I'm sure some software could figure out the individual notes, but I wouldn't be surprised if it transcribed it as nonsense sequence of notes all over on the g, b and e strings, instead of arpeggiated chords as it really is played.

There is just too much going on with a track. Its really strange for us to say this mash up of sounds is "really" A-D-E at the heart of it, and when I play these chords it will suggest that wall of sound you heard on the record. The net is just capturing our biases.

How am I being ‘effed over’? I might be, but I haven’t spotted how.

So Ultimate Guitar is a steal really. I got a lifetime subscription a couple of years ago but I would also sign up for a monthly plan gladly.

I still have a full copy of its archives somewhere collecting dust in my old hard drives.

Beyond the usual web-based suspects (some of which have been named elsewhere in this thread) there are also a number of other tab sharing "communities", including some relevant subreddits that might be the easiest way to spread the word if you're not comfortable uploading to a more directly for-profit service (not that Reddit isn't for-profit too, but guitar tabs certainly aren't their business model and you'd largely be linking to files stored elsewhere rather than uploading them directly).

But a lot of people seem to do what you did also. I suspect it's not super-effective as a distribution model, but if you dig a little it's not hard to find smaller/specialized tab collections on personal websites or general-purpose file hosting services like Google Drive/Dropbox or even at places like Github.

I don't think Google's `filetype:` operator respects guitar-tab-related extensions like `gpx` or `gp5` or `tab` or whatever but using the `inurl:` operator (or just including the tab-specific file extension) combined with the song title or artist name is a good way to find some of these less-centrally located tabs.

Also there are several massive collections available via torrent and on some of those more gray-hat-ish anonymous/one-off file-sharing services. Arguably these may have a more questionable provenance but I suspect the plurality if not the majority of the guitar tabs you'll find shared anywhere online could be traced back to old-school usenet or the first generation of tab-sharing websites that grew out those communities. Ultimate-Guitar for example self-reports as having "chords and tabs to over 1,100,000 songs", and likes to highlight their "official" (licensed) transcriptions (and to be fair they are nothing to sneeze at), but it's probably no coincidence that they don't seem to report the ratio of "official" to "community-provided" transcriptions.

EDIT: Just out of curiosity I checked the file-count reported at the bottom of UG's search page when filtered by file type, I see:

* 18,688 "official" tabs (AFAIK these are the only actually licensed transcriptions)

* 212,408 "Guitar Pro" (.gpx/.gp5/.gp4/.gp3) files; user-generated and many of which probably weren't originally uploaded to UG

* 1,115,129 plain-text transcriptions (801,597 "chords", 313,532 "tabs") that almost certainly date back to Usenet-era communities

The ~20K of licensed/official transcriptions are impressive (both individually and collectively) but represents less than 2% of the overall song catalog (and much less than 2% by file count).