Explain difference to torch.stft

You mentioned in the readme that

Other GPU audio processing tools are torchaudio and tf.signal. But they are not using the neural network approach, and hence the Fourier basis can not be trained.

Can you explain this in more detail, please?

• when would I benefit from the STFT in nnAudio compared to let’s say torch.stft?

• does it make a difference which STFT I use when I am interested in a time domain loss, hence does it change backprop?

Thanks!

[jjhuang-ca]

@carlthome The speech processing community long ago decided that win_length=400 is a good choice for time resolution, so it’s basically standardized for speech recognition. You’re right that FFT needs power of 2, and 512 is the closest to 400. The Hann window is applied to the 400 input samples, zero pad to 512, then FFT. If you look at any speech processing pipelines this is how it’s done (in Kaldi, ESPNet, etc.).

[KinWaiCheuk]

I am curious though about trying nnAudio and will !

Thanks for the interest in nnAudio. nnAudio is still in the early stage of development, if you find any bugs and problems, free feel to ask here again, I will try my best to solve the problems and improve nnAudio.

torch.stft is a really good option, since no extra dependency is required for it unlike torchaudio. What makes nnAudio different from torch.stft is the trainable stft.