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

• URL: http://arxiv.org/abs/2201.09429v2
• Date: Tue, 25 Jan 2022 02:14:49 GMT
• Title: End-to-End Neural Audio Coding for Real-Time Communications
• Authors: Xue Jiang, Xiulian Peng, Chengyu Zheng, Huaying Xue, Yuan Zhang, Yan Lu
• Abstract summary: This paper proposes the TFNet, an end-to-end neural audio system with low latency for real-time communications (RTC) An interleaved structure is proposed for temporal filtering to capture both short-term and long-term temporal dependencies. 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.
• Score: 22.699018098484707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 audio codec with low latency for RTC. It takes an encoder-temporal filtering-decoder paradigm that seldom being 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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