发表机构
Georgia Institute of Technology; The Chinese University of Hong Kong; Tencent Music Entertainment（佐治亚理工学院; 香港中文大学; 腾讯音乐娱乐集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对神经音频编解码器中码本频谱纠缠等问题，提出HARP训练策略，将RVQ阶段分组，在解码器能访问低频时各小组细化目标频带，重建泛音保留连贯性，该策略无需架构改变，性能优于标准RVQ和并行分解。
AI 中文摘要
具有残差向量量化（RVQ）的神经音频编解码器通常对所有频率一视同仁，导致其码本频谱纠缠。截断阶段会去除不可预测的频率混合。并行频带分解通过将音频拆分为独立频带来解决此问题，但会使潜在空间碎片化并失去跨频率连贯性。我们引入了HARP（谐波感知残差划分），这是一种训练策略，将RVQ阶段划分为按频率排序的组，每个组在解码器仍可访问所有低频的同时细化其目标频带。泛音在基音的背景下重建，保留了并行方法所失去的连贯性。HARP无需架构更改，仅修改训练损失，推理与标准RVQ相同。在语音、音乐和一般音频上，HARP优于标准RVQ和并行分解。MUSHRA听力测试也显示出感知上的改进。
英文摘要
Neural audio codecs with residual vector quantization (RVQ) normally treat all frequencies uniformly, so their codebooks become spectrally entangled. Truncating stages then removes an unpredictable mix of frequencies. Parallel band decomposition addresses this by splitting audio into independent bands, but fragments the latent space and loses cross-frequency coherence. We introduce HARP (Harmonic-Aware Residual Partitioning), a training strategy that partitions RVQ stages into frequency-ordered groups where each group refines its target band while the decoder retains access to all lower frequencies. Overtones are reconstructed in the context of their fundamentals, preserving coherence that parallel methods lose. HARP requires no architectural changes; it only modifies the training loss, leaving inference identical to standard RVQ. On speech, music, and general audio, HARP outperforms both standard RVQ and parallel decomposition. MUSHRA listening tests also show perceptual improvements.
CommentsAccepted to Interspeech 2026
