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arXiv · 2201.09429

End-to-End Neural Speech Coding for Real-Time Communications

Xue Jiang·Xiulian Peng·Chengyu Zheng·Huaying Xue·Yuan Zhang·Yan Lu
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Abstract

Deep-learning based methods have shown their advantages in audio coding over traditional ones but limited attention has been paid on real-time communications (RTC). This paper proposes the TFNet, an end-to-end neural speech codec with low latency for RTC. It takes an encoder-temporal filtering-decoder paradigm that has seldom been investigated in audio coding. An interleaved structure is proposed for temporal filtering to capture both short-term and long-term temporal dependencies. Furthermore, with end-to-end optimization, the TFNet is jointly optimized with speech enhancement and packet loss concealment, yielding a one-for-all network for three tasks. Both subjective and objective results demonstrate the efficiency of the proposed TFNet.

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Xue Jiang, Xiulian Peng, Chengyu Zheng, Huaying Xue, Yuan Zhang, Yan Lu. 2022-01-24. End-to-End Neural Speech Coding for Real-Time Communications. https://arxiv.org/abs/2201.09429

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