SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Sounds
As generative audio models grow in complexity, the computational and ecological costs of synthesizing everyday sounds have become increasingly prohibitive, often requiring industrial-scale resources and massive datasets. In this paper, we present SCAPES: a Semantically Conditioned Autoregressive Prior for Environmental Sounds. SCAPES is a lightweight, resource-efficient generative model designed to synthesize high-fidelity environmental textures through high-level semantic control. By operating on the continuous latent manifold of a neural audio codec, our approach bypasses the rigid structural constraints inherent to discrete tokenization. We propose a segmentation strategy that decompos...
| Authors | Esteban Gutiérrez, Lonce Wyse, Frederic Font et al. |
|---|---|
| Primary category | cs.SD |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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