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Speech Enhancement via Residual Dense Generative Adversarial Network

Lin Zhou1,*, Qiuyue Zhong1, Tianyi Wang1, Siyuan Lu1, Hongmei Hu2

1 School of Information Science and Engineering, Southeast University, Nanjing, 210096, China
2 Medizinische Physik and Cluster of Excellence “Hearing4all”, Department of Medical Physics and Acoustics, University of Oldenburg, 26129, Oldenburg, Germany

* Corresponding Author: Lin Zhou. Email: email

Computer Systems Science and Engineering 2021, 38(3), 279-289.


Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.


Cite This Article

L. Zhou, Q. Zhong, T. Wang, S. Lu and H. Hu, "Speech enhancement via residual dense generative adversarial network," Computer Systems Science and Engineering, vol. 38, no.3, pp. 279–289, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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