Notes on my paper; On the Robustness of Deep Convolutional Neural Networks for Music Classification

In this paper I talk about music tagging quite a lot, and audio preprocessing also quite a lot, and some analysis on trained network which is related to music tagging problem again.

Music tagging dataset groundtruth is so wrong

Yeah, because of weakly-labelling it is so incorrect. But how much?

Screen Shot 2017-06-07 at 21.04.31

..about this much. — I manually annotated 4 labels on 500 songs.

Gosh, 70% error? No worries though, it’s sort of ‘weakly-supervised learning’ situation, in which with enough data it’s fine.


But, how much is it fine? — in evaluation?

Screen Shot 2017-06-07 at 20.55.46 evaluation of four instrument tags with my annotation.
blue.dash: eval of four instrument tags with MSD groundtruth
yellow.solid: eval of all tags with MSD groundtruth

Corr(red, blue) = how much it’s fine to use MSD groundtruth for 4 tags.
Corr(blue, yellow) = how much it’s fine to generalise 4-tag result for all-tag result.

And as you see. Well, I’d say this is fine. For sure there’s error (Which can be significant if the difference is subtle).

X vs log(X) if X in [spectrogram, melgram, cqt, …]

tl;dr: use log(x). See this distribution!

Screen Shot 2017-06-07 at 21.17.51

Or, see how much disadvantageous if not log(). Roughly 2x data, which is a lot.

Screen Shot 2017-06-07 at 21.18.42

Spectral whitening (per-frequency stdd)? A-weighting? Should I do some special normalisation for better result?


How similar music tags are according to trained convnet?

Screen Shot 2017-06-07 at 21.21.53

More details on the paper:


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