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This is interesting, but fairly easy to confuse. Esp. would be interesting to see what results come up when you use modified "artistic" spectographs like that o

This is interesting, but fairly easy to confuse. Esp. would be interesting to see what results come up when you use modified "artistic" spectographs like that of Windowlicker by Aphex Twin [1]. One thing I've learned from years of having worked with audio and images is that image representations of audio are horrible representations of it (other than for temporal changes). The results are good though! Good work! :D [1] http://twistedsifter.com/2013/01/hidden-images-embedded-into-songs-spectrographs/ http://twistedsifter.com/2013/01/hidden-images-embedded-into...

cubano 9y ago
> ...image representations of audio are horrible representations of it (other than for temporal changes). Yes, and thus the reason why the classifier was so good at recognizing trance...it's one of the few genres that locks in at around 144bpm.
- Drdrdrq 9y ago
  What would be a better representation?
Pigo 9y ago
It also doesn't help that music from every genre is becoming more homogenized as time goes by [1]. If your comparing by similarity, then this is only going to get more difficult. [1] http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0115255#pone.0115255.s001 http://journals.plos.org/plosone/article?id=10.1371/journal....
skewart 9y ago
I'm curious to hear more about why image representations of music are horrible. What are the problems or limitations? Is there a better way to perform a similar kind of "dimensionality reduction" on music?
ssalazar 9y ago
> image representations of audio are horrible representations of it The spectrogram is just a series of FFTs taken over time; encoding it as a bitmap doesn't really change this, aside from precision issues. Any other representation of the audio is derived from either the original time-domain signal or the FFT. Indeed, humans can't reliably map raw waveforms or spectrograms to intuitive musical phenomena. But a CNN should be able to derive meaningful features from these basic representations on its own.

cubano 9y ago

> ...image representations of audio are horrible representations of it (other than for temporal changes). Yes, and thus the reason why the classifier was so good at recognizing trance...it's one of the few genres that locks in at around 144bpm.

Drdrdrq 9y ago
What would be a better representation?

Pigo 9y ago

It also doesn't help that music from every genre is becoming more homogenized as time goes by [1]. If your comparing by similarity, then this is only going to get more difficult. [1] http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0115255#pone.0115255.s001 http://journals.plos.org/plosone/article?id=10.1371/journal....

skewart 9y ago

I'm curious to hear more about why image representations of music are horrible. What are the problems or limitations? Is there a better way to perform a similar kind of "dimensionality reduction" on music?

ssalazar 9y ago

> image representations of audio are horrible representations of it The spectrogram is just a series of FFTs taken over time; encoding it as a bitmap doesn't really change this, aside from precision issues. Any other representation of the audio is derived from either the original time-domain signal or the FFT. Indeed, humans can't reliably map raw waveforms or spectrograms to intuitive musical phenomena. But a CNN should be able to derive meaningful features from these basic representations on its own.