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Earlier quoted context omitted.
As an amateur musician I see this as a missing holy grail killer app. I'd love to have it just to pull the chords out of some of my own old recordings that I can't figure out how to repeat. If anyone knows of any apps (even prototypes) that can do this, please provide links.
I may have to blow the dust off of my prototype.
You would think getting the notes and converting them to chords, and tablature, would be one of those "exactly suited for neural nets" type of problem. If you want the chords to "Here comes the sun," you can find dozens of hits, but try something slightly obscure and they are hard to come by. (People with great ears have no idea what I am talking about.)
Earlier quoted context omitted.
I've worked on this problem for some time on a personal project, and I'm pretty convinced you can basically solve this problem without deep learning or AI techniques, and instead use non-negative matrix factorization[0] as a bank of note templates (from their spectrograms). I have a fairly well working proof of concept and the approach is supported by the literature. [0]: https://en.wikipedia.org/wiki/Non-negative_ma…
As an amateur musician I see this as a missing holy grail killer app. I'd love to have it just to pull the chords out of some of my own old recordings that I can't figure out how to repeat. If anyone knows of any apps (even prototypes) that can do this, please provide links.
Alternatively if you want a flexible free (GPLv2) option with source code, consider the Chordino plugin (http://www.isophonics.net/nnls-chroma, C++ code at https://github.com/c4dm/nnls-chroma) which you can run in Sonic Visualiser (https://www.sonicvisualiser.org/), or indeed in Audacity.
There is a reference describing the Chordino method in the page linked above, but roughly it's not too far from the description of the parent poster - a non-negative least-squares method produces a frame-by-frame semitone-scaled decomposition which is then matched against templates and turned into a chord sequence using a hidden Markov model. Some sort of intelligent smoothing like this is certainly needed, the raw template matching is not especially useful on its own.
This type of method is now routinely outperformed by neural networks (see e.g. these MIREX evaluation results from 2018 which compare Chordino with a few other academic methods https://www.music-ir.org/mirex/wiki/2018:Audio_Chord_Estimat...) but I would suggest that it's still good enough to be useful - and I encourage the parent to continue their work, as it's an interesting area to explore.
You would think getting the notes and converting them to chords, and tablature, would be one of those "exactly suited for neural nets" type of problem. If you want the chords to "Here comes the sun," you can find dozens of hits, but try something slightly obscure and they are hard to come by. (People with great ears have no idea what I am talking about.)
Thus propagating the NN "If all you have is a hammer" trend. I don't quite know the nuance of music theory, but could you not get away with traditional Fourier analysis? You just need to decompose the song into its constituent frequency "bins" right?
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.
You would think getting the notes and converting them to chords, and tablature, would be one of those "exactly suited for neural nets" type of problem. If you want the chords to "Here comes the sun," you can find dozens of hits, but try something slightly obscure and they are hard to come by. (People with great ears have no idea what I am talking about.)
I don't think finding individual notes / the inherent notes within a chord is likely that difficult. The problem with this sort of thing is the nuance that's really involved - a player with a decent ear will be able to tell if the same A note is played on an open string, on the lower, fatter strings, or further up the neck. With chords, you also need to start considering voice leading (where you're specifically picki…
Oh so this is not what I thought it is. I thought it actually figures out the chords for any given song.
Neat shortcut though. Thanks for the effort.
So yes there are definitely a use case for all the new media players to show chords
You would think getting the notes and converting them to chords, and tablature, would be one of those "exactly suited for neural nets" type of problem. If you want the chords to "Here comes the sun," you can find dozens of hits, but try something slightly obscure and they are hard to come by. (People with great ears have no idea what I am talking about.)
I don't think finding individual notes / the inherent notes within a chord is likely that difficult. The problem with this sort of thing is the nuance that's really involved - a player with a decent ear will be able to tell if the same A note is played on an open string, on the lower, fatter strings, or further up the neck. With chords, you also need to start considering voice leading (where you're specifically picki…