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arXiv · 2610.15243

HE-NAC: High-Efficiency Neural Audio Coding via Hierarchical and Residual Multi-Band Quantization

Darius Pétermann·Wootaek Lim·Seungkwon Beack·Minje Kim
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Abstract

High-fidelity audio coding requires faithful reconstruction of perceptually important high-frequency components, which are often lost due to the aggressive temporal decimation and quantization of neural codecs. State-of-the-art neural audio coding methods rely on adversarial learning to hallucinate these missing components, yielding perceptually plausible but imprecise reconstructions; a limitation for high-fidelity music applications. As a remedy, we propose high-efficiency neural audio coding (HE-NAC), which provides a multi-band extension of the popular residual vector quantization (RVQ) scheme. HE-NAC introduces an effective training mechanism that harmonizes a bitrate-free blind upsampling process, residual learning to compensate it with minimal bitrate, and a multi-head decoder architecture for band-specific reconstruction. As a result, HE-NAC achieves a balanced trade-off, reliably reconstructing the core-band ($\leq$ 3 kHz) component while preserving overall sound fidelity. Subjective listening tests and objective evaluations demonstrate that HE-NAC outperforms both the DAC baseline and MPEG HE-AAC at equal or lower bitrates.

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Darius Pétermann, Wootaek Lim, Seungkwon Beack, Minje Kim. 2026-10-11. HE-NAC: High-Efficiency Neural Audio Coding via Hierarchical and Residual Multi-Band Quantization. https://doi.org/10.1109/taslpro.2026.3739174

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