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This repository was archived by the owner on Sep 1, 2024. It is now read-only.
This repository was archived by the owner on Sep 1, 2024. It is now read-only.

How to define the weight coefficient in mask loss? #27

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@dengyuanjie

Hello, could you please explain the meaning of the weights here?

This coefficient is not included in the paper, and I have found that it is not necessary to calculate this weight in the test.py.

     # calculate loss weighting coefficient        
     if self.opt.weighted_loss:
        weight1 = torch.log1p(torch.norm(audio_mix_spec1[:,:,:-1,:], p=2, dim=1)).unsqueeze(1).repeat(1,2,1,1)
        weight1 = torch.clamp(weight1, 1e-3, 10)
        weight2 = torch.log1p(torch.norm(audio_mix_spec2[:,:,:-1,:], p=2, dim=1)).unsqueeze(1).repeat(1,2,1,1)
        weight2 = torch.clamp(weight2, 1e-3, 10)
    else:
        weight1 = None
        weight2 = None

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