TCNN Temporal convolutional neural network for real-time speech enhancement in the time domain

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Deep Learning TCNN
Overview
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Comments
  • Overfitting Problem

    Overfitting Problem

    Hello. Thanks for this model architecture code. I am just very beginner in this area and not much familiar with Deep Learnning standard procedures and all.

    Actually, I have converted this code - https://github.com/haoxiangsnr/IRM-based-Speech-Enhancement-using-LSTM into time domain and then used your model architecture code for this. I am using google Colab platform for this.

    I have used TIMIT dataset which is having 8732 utterances and randomly mixed with UrbanSound8K noises at -5dB,-4dB,-3dB,-2dB,-1dB,0dB and 1dB, so I am having 8732 noisy speeches. Then I convert it into overlapping frames. And output proceeds.

    But I am not sure whether it is overfitting - After 600 epochs of training validation, average PESQ score obtained is 2.13. Average PESQ between clean speech and noisy speech of UrbanSound8k + TIMIT clean speech is around 1.8 . (On each epoch 900 noisy utterences are trained and on next epoch utterences are shuffled and it is trained on other 900 utterences)

    But upon testing, I use NOIZEUS database which is unseen to the TCNN network. I am getting very low PESQ score of 1.42 after loading checkpoints. Also when i run same inference script I get different PESQ scores like 1.3, 1.4 or 1.5 something like that ! On same model checkpoint.

    Any suggestions why this is the case ? It would be very helpful.

    Thanks.

    opened by HardeyPandya 7
Owner
凌逆战
凌逆战
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