Geometric Iterative Retrieval for Neural Audio Codec Resynthesis
Neural audio codecs based on Residual Vector Quantization (RVQ) have become the dominant discrete representation for token-based general audio generation, yet resynthesizing high-quality audio from coarse codec tokens remains an open problem and bounds the fidelity of every system that generates them. Prior work has framed resynthesis as a choice between discrete token prediction and continuous regression. We argue that this dichotomy is incomplete and introduce geometric iterative retrieval, a paradigm that uses the RVQ layer hierarchy itself as a natural iterative decomposition in continuous codebook space. Rather than classifying over discrete vocabularies or regressing to a single tar...
| Authors | Leo Schmidt-Traub, Frédéric Berdoz, Luca A. Lanzendörfer et al. |
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| Primary category | cs.SD |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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