1. An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec
- Author
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Xu, Linping, Jiang, Jiawei, Zhang, Dejun, Xia, Xianjun, Chen, Li, Xiao, Yijian, Ding, Piao, Song, Shenyi, Yin, Sixing, and Sohel, Ferdous
- Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing ,Computer Science - Sound - 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., Comment: INTERSPEECH 2023
- Published
- 2024