An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec
<|reference_start|>An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec: 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.<|reference_end|>
@article{xu2024an,
    title={An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec},
    author={Linping Xu, Jiawei Jiang, Dejun Zhang, Xianjun Xia, Li Chen, Yijian
  Xiao, Piao Ding, Shenyi Song, Sixing Yin, Ferdous Sohel},
    journal={arXiv preprint arXiv:2402.01271},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01271},
    primaryClass={eess.AS cs.SD}
}