eloimoliner / CQT_pytorch

Pytorch implementation of the invertible CQT based on Non-stationary Gabor filters
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how to correctly normalize outputs #3

Open MultiTrickFox opened 1 year ago

MultiTrickFox commented 1 year ago

given the basic conversion: cqt = CQT_nsgt(numocts, binsoct, mode="matrix_complete", fs=fs, audiolen=Ls, device="cpu", dtype=torch.float32) coeffs = cqt.fwd(data) data = cqt.bwd(coeffs)

checking the coeffs array seems its not normalized; print(coeffs.real.min(), coeffs.real.max())

tensor(-101.7243) tensor(140.4326)

Is there a way to quickly and correctly fix this, including being used in bwd() later on too..