In this PR I add optional FIRFilter input to STFTLoss, this filter automatically fills self.prefilter if available and sets it to None if not provided. If only perceptual_weighting flag is set, self.prefilter is set with internally constructed FIRFilter.
If both an external FIRFilter is provided and perceptual_weighting flag is set, an nn.Sequential variation (that allows for two inputs) is constructed to run both filters sequentially.
Tested on some audio input and appears to be working as expected. Below are some spectrograms of the different variations.
No Filter:
Only Perceptual Weighting:
Only External 4.5k Lowpass Filter:
Both Filters:
Also included are the changes to auraloss.perceptual.FIRFilter which allows for for butterworth filter construction and a FIRSequential class in auraloss.utils that inherits from nn.Sequential and allows for multiple inputs.
I haven't tried using this branch in a model yet but it has worked returning losses as expected in my testing of just this repo.
In this PR I add optional FIRFilter input to STFTLoss, this filter automatically fills self.prefilter if available and sets it to None if not provided. If only perceptual_weighting flag is set, self.prefilter is set with internally constructed FIRFilter.
If both an external FIRFilter is provided and perceptual_weighting flag is set, an nn.Sequential variation (that allows for two inputs) is constructed to run both filters sequentially.
Tested on some audio input and appears to be working as expected. Below are some spectrograms of the different variations.
No Filter:
Only Perceptual Weighting:
Only External 4.5k Lowpass Filter:
Both Filters:
Also included are the changes to
auraloss.perceptual.FIRFilterwhich allows for for butterworth filter construction and a FIRSequential class inauraloss.utilsthat inherits from nn.Sequential and allows for multiple inputs.I haven't tried using this branch in a model yet but it has worked returning losses as expected in my testing of just this repo.