4 ms·
The idea is that the vertical axis of the spectrogram is basically already an hierarchical set of features (in scale/frequency). Then convolutions on that is a
by highd 10y ago
The idea is that the vertical axis of the spectrogram is basically already an hierarchical set of features (in scale/frequency). Then convolutions on that is a lot like how DenseNets combine hierarchical features.
I agree it seems a little jank, but the features are pretty good - and a lot of network architectures / training techniques are most practiced in an image processing context.
- Despoisj 10y agoThanks for your inputs, it's true that we can use convolutions on raw waveform, however the main reason I've used a spectrogram was to work on precomputed relevant features as highd pointed out, instead of running the convolution on lots of data.