HILCodec：高保真轻量级神经音频编解码器
HILCodec: High-Fidelity and Lightweight Neural Audio Codec
AI总结：
本文提出HILCodec，通过方差受限设计解决SEANet编解码器性能不随深度增加的问题，并引入无失真判别器，实现了实时流式的高保真轻量级神经音频编解码。
摘要：
端到端神经音频编解码器的最新进展使得在极低比特率下压缩音频并高保真重建输出音频成为可能。然而，这些改进往往以增加模型复杂度为代价。本文识别并解决了现有神经音频编解码器存在的问题。我们发现基于SEANet的编解码器性能并未随网络深度增加而持续提升。我们分析了该现象的根本原因，并提出了方差受限设计。此外，我们揭示了先前波形域判别器中的各种失真，并提出了一种新颖的无失真判别器。最终模型HILCodec是一种实时流式音频编解码器，在多种比特率和音频类型上展现出最先进的品质。
英文摘要：
The recent advancement of end-to-end neural audio codecs enables compressing audio at very low bitrates while reconstructing the output audio with high fidelity. Nonetheless, such improvements often come at the cost of increased model complexity. In this paper, we identify and address the problems of existing neural audio codecs. We show that the performance of the SEANet-based codec does not increase consistently as the network depth increases. We analyze the root cause of such a phenomenon and suggest a variance-constrained design. Also, we reveal various distortions in previous waveform domain discriminators and propose a novel distortion-free discriminator. The resulting model, HILCodec, is a real-time streaming audio codec that demonstrates state-of-the-art quality across various bitrates and audio types.
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。
