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arXiv · 2402.01271

An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec

Linping Xu·Jiawei Jiang·Dejun Zhang·Xianjun Xia·Li Chen·Yijian Xiao·Piao Ding·Shenyi Song·Sixing Yin·Ferdous Sohel
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Abstract

Recently, neural networks have proven to be effective in performing speech coding task at low bitrates. However, under-utilization of intra-frame correlations and the error of quantizer specifically degrade the reconstructed audio quality. To improve the coding quality, we present an end-to-end neural speech codec, namely CBRC (Convolutional and Bidirectional Recurrent neural Codec). An interleaved structure using 1D-CNN and Intra-BRNN is designed to exploit the intra-frame correlations more efficiently. Furthermore, Group-wise and Beam-search Residual Vector Quantizer (GB-RVQ) is used to reduce the quantization noise. CBRC encodes audio every 20ms with no additional latency, which is suitable for real-time communication. Experimental results demonstrate the superiority of the proposed codec when comparing CBRC at 3kbps with Opus at 12kbps.

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Linping Xu, Jiawei Jiang, Dejun Zhang, Xianjun Xia, Li Chen, Yijian Xiao, Piao Ding, Shenyi Song, Sixing Yin, Ferdous Sohel. 2024-02-02. An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec. https://arxiv.org/abs/2402.01271

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