{"ID":6620657,"CreatedAt":"2026-07-15T01:01:48.440468303Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.12725","arxiv_id":"2607.12725","title":"Neural Morphing: Sequence-Optimized Token-Level Morphing in Neural Audio Codecs","abstract":"Neural audio codecs were originally developed for high-fidelity compression; however, their latent token representations and expressive decoders also constitute a powerful substrate for controllable audio transformation. This work introduces Neural Morphing, a training-free token-domain audio effect that selects residual-vector-quantized (RVQ) token grains from a user palette and decodes the edited stream through a pretrained codec. The method combines an RVQ-group transfer policy that separates coarse, middle, and fine codebook groups with a continuity-constrained sequence matcher that replaces independent greedy selection with bounded beam search. The intended output is a controlled hybrid: the source preserves rhythmic organization while the palette contributes timbral color and residual detail. We focus on the implementation and realtime behavior of a deployable VST3/AU system, including chunked rendering, palette-size scaling, and backend health checks.","short_abstract":"Neural audio codecs were originally developed for high-fidelity compression; however, their latent token representations and expressive decoders also constitute a powerful substrate for controllable audio transformation. This work introduces Neural Morphing, a training-free token-domain audio effect that selects residu...","url_abs":"https://arxiv.org/abs/2607.12725","url_pdf":"https://arxiv.org/pdf/2607.12725v1","authors":"[\"Emmanouil Karystinaios\"]","published":"2026-07-14T12:55:18Z","proceeding":"cs.SD","tasks":"[\"cs.SD\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}