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>Recognizing chords seems like a useful thing to solve? It's also pretty easy to teach and learn for a practicing musician but much more difficult to teach a m

>Recognizing chords seems like a useful thing to solve? It's also pretty easy to teach and learn for a practicing musician but much more difficult to teach a machine, since you run into issues with blind source separation. Speech transcription is good enough provided we have enough preprocessing power, assume a single speaker, and know the language beforehand and have trained the model on a large number of previous speakers with the same dialect/accent. In music, you don't know how many speakers there are (instruments playing), the dialect/accent (orchestration/chord voicing) changes on the fly, representations are non-unique and contextual, and artists intentionally subvert expected results to make good music. Humans are just better at this and easier to train to do it than computers, for the moment.

yorwba 7y ago
On the other hand, most instruments are built for repeatability, so you can play the same note twice and have it sound pretty much the same. Humans produce speech with a resonant cavity made of soft tissue, so a sound like "aaah" corresponds to an unsharp range of tongue shapes etc. and saying the same thing twice is pretty much guaranteed to sound quite different. Being able to separate individual notes of a musical piece into sharply defined buckets (keys of a piano) or one-dimensional subspaces (finger position on stringed instruments like guitars) simplifies the source separation problem a lot. That representations are contextual and subject to interpretation by the artist is a harder problem (as discussed in TFA), but it should be possible to treat it separately from the pure chord recognition problem. (E.g. it would be easy to take notation and a matching MIDI file and then pretend that it's the output of the recognition step which the original notation should be recovered from.)
- unlinked_dll 7y ago
  The buckets are more flat than sharply defined (ha). That's part of the problem. Information is lost from notation to pitch to recording, and I suspect one could prove that these transforms don't preserve topology and are not homophormisms that can be easily reversed. To use your example, finger position is not a one dimensional subspace on a guitar. There are between 1-6 ways to play a given pitch even assuming standard tuning on a six string guitar, which is not a safe assumption to make. But the issue with chord transcription is one at the heart of source separation outside of VC demos, which is the causality problem that no one likes to talk about. To separate into N sources you need to know a priori that there are N sources to separate into. This is not a trivial thing to predict in chord voicings, where N changes and is not predictable. Then you need to make a best guess at which instrument the pitches fall into, which may be shared. This is something that even humans fall victim too. Untrained listeners are very bad at quantifying sources in an ensemble, and even trained listeners struggle with notating chord voicings with decent accuracy.

yorwba 7y ago

On the other hand, most instruments are built for repeatability, so you can play the same note twice and have it sound pretty much the same. Humans produce speech with a resonant cavity made of soft tissue, so a sound like "aaah" corresponds to an unsharp range of tongue shapes etc. and saying the same thing twice is pretty much guaranteed to sound quite different. Being able to separate individual notes of a musical piece into sharply defined buckets (keys of a piano) or one-dimensional subspaces (finger position on stringed instruments like guitars) simplifies the source separation problem a lot. That representations are contextual and subject to interpretation by the artist is a harder problem (as discussed in TFA), but it should be possible to treat it separately from the pure chord recognition problem. (E.g. it would be easy to take notation and a matching MIDI file and then pretend that it's the output of the recognition step which the original notation should be recovered from.)

unlinked_dll 7y ago
The buckets are more flat than sharply defined (ha). That's part of the problem. Information is lost from notation to pitch to recording, and I suspect one could prove that these transforms don't preserve topology and are not homophormisms that can be easily reversed. To use your example, finger position is not a one dimensional subspace on a guitar. There are between 1-6 ways to play a given pitch even assuming standard tuning on a six string guitar, which is not a safe assumption to make. But the issue with chord transcription is one at the heart of source separation outside of VC demos, which is the causality problem that no one likes to talk about. To separate into N sources you need to know a priori that there are N sources to separate into. This is not a trivial thing to predict in chord voicings, where N changes and is not predictable. Then you need to make a best guess at which instrument the pitches fall into, which may be shared. This is something that even humans fall victim too. Untrained listeners are very bad at quantifying sources in an ensemble, and even trained listeners struggle with notating chord voicings with decent accuracy.