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JACIII Vol.25 No.1 pp. 130-137
doi: 10.20965/jaciii.2021.p0130
(2021)

Paper:

An Improved Fully Convolutional Network Based on Post-Processing with Global Variance Equalization and Noise-Aware Training for Speech Enhancement

Wenlong Li, Kaoru Hirota, Yaping Dai, and Zhiyang Jia

School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China

Corresponding author

Received:
October 25, 2020
Accepted:
November 20, 2020
Published:
January 20, 2021
Keywords:
speech enhancement, fully convolutional network, post-processing with global variance equalization, noise-aware training
Abstract

An improved fully convolutional network based on post-processing with global variance (GV) equalization and noise-aware training (PN-FCN) for speech enhancement model is proposed. It aims at reducing the complexity of the speech improvement system, and it solves overly smooth speech signal spectrogram problem and poor generalization capability. The PN-FCN is fed with the noisy speech samples augmented with an estimate of the noise. In this way, the PN-FCN uses additional online noise information to better predict the clean speech. Besides, PN-FCN uses the global variance information, which improve the subjective score in a voice conversion task. Finally, the proposed framework adopts FCN, and the number of parameters is one-seventh of deep neural network (DNN). Results of experiments on the Valentini-Botinhaos dataset demonstrate that the proposed framework achieves improvements in both denoising effect and model training speed.

Cite this article as:
Wenlong Li, Kaoru Hirota, Yaping Dai, and Zhiyang Jia, “An Improved Fully Convolutional Network Based on Post-Processing with Global Variance Equalization and Noise-Aware Training for Speech Enhancement,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.1, pp. 130-137, 2021.
Data files:
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Last updated on Sep. 19, 2021