SuperCodec：一种带有选择性反向投影网络的神经语音编解码器
SuperCodec: A Neural Speech Codec with Selective Back-Projection Network
AI总结：
提出神经语音编解码器SuperCodec，通过引入选择性上采样和下采样反向投影模块替换标准上下采样层，在低比特率下实现了优于现有方法的语音重建质量。
摘要：
神经语音编码是一个快速发展的课题，其中最先进的方法现在展现出比传统方法更优的压缩性能。尽管取得了显著进展，但现有方法在保留和重建最佳重建所需的精细细节方面仍存在局限性，尤其是在低比特率下。在本研究中，我们引入了SuperCodec，一种在低比特率下实现最先进性能的神经语音编解码器。它采用一种带有选择性特征融合的新型反向投影方法以增强表示。具体而言，我们提出使用选择性上采样反向投影（SUBP）和选择性下采样反向投影（SDBP）模块，分别替换编码器和解码器中的标准上采样和下采样层。实验结果表明，我们的方法优于在各种比特率下运行的现有神经语音编解码器。具体而言，我们提出的方法在1 kbps下能获得比3.2 kbps的Lyra V2和6 kbps的Encodec质量更高的重建语音。
英文摘要：
Neural speech coding is a rapidly developing topic, where state-of-the-art approaches now exhibit superior compression performance than conventional methods. Despite significant progress, existing methods still have limitations in preserving and reconstructing fine details for optimal reconstruction, especially at low bitrates. In this study, we introduce SuperCodec, a neural speech codec that achieves state-of-the-art performance at low bitrates. It employs a novel back projection method with selective feature fusion for augmented representation. Specifically, we propose to use Selective Up-sampling Back Projection (SUBP) and Selective Down-sampling Back Projection (SDBP) modules to replace the standard up- and down-sampling layers at the encoder and decoder, respectively. Experimental results show that our method outperforms the existing neural speech codecs operating at various bitrates. Specifically, our proposed method can achieve higher quality reconstructed speech at 1 kbps than Lyra V2 at 3.2 kbps and Encodec at 6 kbps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
