Speech Dereverberation Using Fully Convolutional Networks

Ori Ernst; Shlomo E. Chazan; Sharon Gannot; Jacob Goldberger

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arXiv · 1803.08243

Speech Dereverberation Using Fully Convolutional Networks

Ori Ernst·Shlomo E. Chazan·Sharon Gannot·Jacob Goldberger

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Abstract

Speech derverberation using a single microphone is addressed in this paper. Motivated by the recent success of the fully convolutional networks (FCN) in many image processing applications, we investigate their applicability to enhance the speech signal represented by short-time Fourier transform (STFT) images. We present two variations: a "U-Net" which is an encoder-decoder network with skip connections and a generative adversarial network (GAN) with U-Net as generator, which yields a more intuitive cost function for training. To evaluate our method we used the data from the REVERB challenge, and compared our results to other methods under the same conditions. We have found that our method outperforms the competing methods in most cases.

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BibTeX RIS

Ori Ernst, Shlomo E. Chazan, Sharon Gannot, Jacob Goldberger. 2018-03-22. Speech Dereverberation Using Fully Convolutional Networks. https://arxiv.org/abs/1803.08243

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