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why use the reverberated speech signal as the training target #16
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pls check sms_wsj_plus.py which is the latest dataset for jointly speech separation, denoising and dereverberation. The code you referred to is old and not used in SpatialNet. |
thanks for your response!
thanks! |
The babble noise is diffuse, while the target speech signals are directional, that is the key clue for the model to learn to distinguish them.
The babble noise is diffuse not directional, so it doesn't need to be convolved with rirs. And we use the method implemented in https://github.com/Audio-WestlakeU/NBSS/blob/main/data_loaders/utils/diffuse_noise.py to make it diffuse. |
thanks for your response! |
hi,
it a great amazing project, thanks for your effort.
When I looked at the code, I found that the training target signal was reverberated speech. (https://github.com/Audio-WestlakeU/NBSS/blob/af66db92bb9d6f72f7100d613d3df38c40b10b09/data_loaders/ss_semi_online_dataset.py#L294C27-L294C27)
I wander why not use clean speech as the training target, as it would not only separate speakers, but also remove reverberation and even noise.
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