动态分配码率，让语音编码在噪声环境下更省带宽且保质。
Towards Bitrate-Efficient and Noise-Robust Speech Coding with Variable Bitrate RVQ
- 按帧动态调整码率，优先保留语音关键成分
- 噪声环境下比特率效率提升，主观听感更清晰
- 适合实际场景中带噪语音的高效压缩
残差向量量化（RVQ）已成为神经语音与音频编码的主流方法，实现高保真压缩。然而，现实中的噪声会降低压缩效率，传统编码器均匀分配比特，浪费在不影响理解的噪声上。本文提出可变码率残差向量量化（VRVQ）框架，动态调整每帧码率以优化率失真权衡。相比固定码率（CBR）RVQ，该方法优先保留语音关键成分，抑制残差噪声，并集成特征去噪模块进一步增强抗噪能力。实验表明，VRVQ在噪声条件下优于传统方法，提升了压缩效率与感知质量。样本见项目页：https://yoongi43.github.io/noise_robust_vrvq/
原文摘要 · Abstract (English)
Residual Vector Quantization (RVQ) has become a dominant approach in neural speech and audio coding, providing high-fidelity compression. However, speech coding presents additional challenges due to real-world noise, which degrades compression efficiency. Standard codecs allocate bits uniformly, wasting bitrate on noise components that do not contribute to intelligibility. This paper introduces a Variable Bitrate RVQ (VRVQ) framework for noise-robust speech coding, dynamically adjusting bitrate per frame to optimize rate-distortion trade-offs. Unlike constant bitrate (CBR) RVQ, our method prioritizes critical speech components while suppressing residual noise. Additionally, we integrate a feature denoiser to further improve noise robustness. Experimental results show that VRVQ improves rate-distortion trade-offs over conventional methods, achieving better compression efficiency and perceptual quality in noisy conditions. Samples are available at our project page: https://yoongi43.github.io/noise_robust_vrvq/.
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