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Interesting. I wonder how well a logistic regression that spits out masks would perform in the source separation task. Also a bit surprising to see that they
by a-dub 8y ago
Interesting. I wonder how well a logistic regression that spits out masks would perform in the source separation task.
Also a bit surprising to see that they had to STFT the audio before feeding it into a convnet. I thought half the point of convnets was that they figure out how to do spectral domain representations on their own...
- black_puppydog 8y agoin theory yes, but in practice, giving the network the full information in the right format is crucial to have it train well and quickly.
- a-dub 8y agoisn't that supposed to be the magic of convnets though? they _figure out_ the right format. instead of doing feature engineering, like stfts and mel warping, you do stuff like build convolution layers into an ann and let it sort it out?
- WhiteNoiz3 8y agoI've been messing around with audio nets and that was the first thing that surprised me too - convnets don't work nearly as well (out of the box) for audio as they do for images. This article has some good reasons why audio data is different from image data: https://towardsdatascience.com/whats-wrong-with-spectrograms-and-cnns-for-audio-processing-311377d7ccd https://towardsdatascience.com/whats-wrong-with-spectrograms...