改进测试时的量化编码，提升音频合成质量。
Improving Test-Time Performance of RVQ-based Neural Codecs
- 设计新编码算法，优化测试时代码选择以降低误差。
- 实验表明该方法有效减少量化误差并提升音质。
- 适合关注音频编码细节优化的研究者与工程师。
残差向量量化（RVQ）技术在近期神经音频编解码器中起核心作用。由于各量化层级间的层次结构，这些模型能用少量码本生成高保真音频。本文提出一种新的编码算法，旨在提升测试时RVQ类神经编解码器的合成质量。首先指出传统方法生成的量化向量存在次优性，证明通过选择不同代码集可减轻量化误差。随后提出新编码算法，用于寻找实现更低量化误差的离散代码组合。将该方法应用于预训练模型，并通过多种指标评估其有效性。实验结果验证：该方法不仅降低量化误差，还显著改善音频合成质量。
原文摘要 · Abstract (English)
The residual vector quantization (RVQ) technique plays a central role in recent advances in neural audio codecs. These models effectively synthesize high-fidelity audio from a limited number of codes due to the hierarchical structure among quantization levels. In this paper, we propose an encoding algorithm to further enhance the synthesis quality of RVQ-based neural codecs at test-time. Firstly, we point out the suboptimal nature of quantized vectors generated by conventional methods. We demonstrate that quantization error can be mitigated by selecting a different set of codes. Subsequently, we present our encoding algorithm, designed to identify a set of discrete codes that achieve a lower quantization error. We then apply the proposed method to pre-trained models and evaluate its efficacy using diverse metrics. Our experimental findings validate that our method not only reduces quantization errors, but also improves synthesis quality.
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