Multi-Scale Temporal Frequency Convolutional Network With Axial Attention for Speech Enhancement

Related tags

Text Data & NLP dns-challenge speech-enhancement acoustic-echo-cancellation
Overview
Comments
  • about ERB band filter bank

    about ERB band filter bank

    opened by jzi040941 3
  • CUDA out of memory when using the network to train

    CUDA out of memory when using the network to train

    Hello,

    First of all, thank you for proving the implementation. It was very helpful to understand the paper.

    I had one question though. When I was trying to train the network using 30-second 48kHz audio, I always run into CUDA out of memory error, even if the batch size is set to 1. Have you seen that in your experiments or do you have any advice maybe?

    Anything will be greatly appreciated!

    opened by nubma 3
  • erb.py reported a error :expected np.ndarray (got tuple)

    erb.py reported a error :expected np.ndarray (got tuple)

    The report is as follows:

      File "E:/code_paper/MTFAA-Net-main/erb.py", line 24, in __init__
    filter = th.from_numpy(filter).float()
TypeError: expected np.ndarray (got tuple)

    The error occurred on line 24 of erb.py. filter = th.from_numpy(filter).float() "filter" is a tuple has two members.

    My Python version is 3.9.12. My spafe version is 0.2.0. My torch version is 1.12.1.

    Hope you can teach me.

    invalid 
    opened by FragrantRookie 2
  • Lincense

    Lincense

    Hi @echocatzh

    I think there are many people supposed to use your awesome work for both commercial and non-commercial purposes it would be great for people who use this and of course for you as well if you could add an explicit License would you be able to add the license file?

    opened by jzi040941 1
  • Did you normalize signals when you calculate loss?

    Did you normalize signals when you calculate loss?

    Hi, thanks for you great work. I find that the loss decrease hardly when I train your MTFAA, I dont normalize signals when I calculate loss. Maybe I should normalize signals like 《Data augmentation and loss normalization for deep noise suppression》，I want to know your way to calculate loss.

    opened by YangangCao 1
  • question about the network

    question about the network

    thanks for your code, there is a problem still confuse me, the input of the u-net structure is the magnitude after the phase encoder, but the output of the u-net have two-stage mask, one is magnitude mask, the other is phase mask and magnitude mask, I am confusing that there is no phase information input to the u-net structure, how can it get the correct phase mask? or after phase encoder, although the output is magnitude, but it includes phase information?

    opened by wendongj 1
Owner
Shimin Zhang
Speech Enhancement
Shimin Zhang
GitHub
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