Convolutional fusion network for monaural speech enhancement.

Neural Networks : the Official Journal of the International Neural Network Society
Yang XianSyed Mohsen Naqvi

Abstract

Convolutional neural network (CNN) based methods, such as the convolutional encoder-decoder network, offer state-of-the-art results in monaural speech enhancement. In the conventional encoder-decoder network, large kernel size is often used to enhance the model capacity, which, however, results in low parameter efficiency. This could be addressed by using group convolution, as in AlexNet, where group convolutions are performed in parallel in each layer, before their outputs are concatenated. However, with the simple concatenation, the inter-channel dependency information may be lost. To address this, the Shuffle network re-arranges the outputs of each group before concatenating them, by taking part of the whole input sequence as the input to each group of convolution. In this work, we propose a new convolutional fusion network (CFN) for monaural speech enhancement by improving model performance, inter-channel dependency, information reuse and parameter efficiency. First, a new group convolutional fusion unit (GCFU) consisting of the standard and depth-wise separable CNN is used to reconstruct the signal. Second, the whole input sequence (full information) is fed simultaneously to two convolution networks in parallel, and their ...Continue Reading

References

Jan 20, 2015·IEEE/ACM Transactions on Audio, Speech, and Language Processing·Yuxuan WangDeLiang Wang
Dec 6, 2016·IEEE/ACM Transactions on Audio, Speech, and Language Processing·Xiao-Lei Zhang, DeLiang Wang
Jan 7, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Vijay BadrinarayananRoberto Cipolla
Jul 7, 2017·The Journal of the Acoustical Society of America·Jitong Chen, DeLiang Wang
Jul 30, 2019·IEEE/ACM Transactions on Audio, Speech, and Language Processing·Ke TanDeLiang Wang
Sep 6, 2019·IEEE/ACM Transactions on Audio, Speech, and Language Processing·Yi Luo, Nima Mesgarani
Jul 1, 2019·IEEE/ACM Transactions on Audio, Speech, and Language Processing·Ashutosh Pandey, DeLiang Wang

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