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arXiv Machine Learning By Leo Schmidt-Traub, Fr\'ed\'eric Berdoz, Luca A. Lanzend\"orfer, Roger Wattenhofer

Geometric Iterative Retrieval for Neural Audio Codec Resynthesis

Read the original on arXiv Machine Learning →

The paper introduces geometric iterative retrieval, a new approach for resynthesizing high‑quality audio from coarse Residual Vector Quantization (RVQ) codec tokens. Instead of choosing between discrete token prediction or continuous regression, the method performs contrastive retrieval within the continuous codebook space, leveraging the RVQ hierarchy as an iterative decomposition. Experiments on speech and music codec restoration tasks demonstrate that this technique outperforms both single‑pass token prediction and one‑step regression baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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