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Life sciences · Preprint
arXiv · September 4, 2026
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This is an unrefereed technical preprint presenting SCAPES, a lightweight generative model for environmental sound synthesis using continuous normalizing flows on a neural audio codec latent space. The work is a methods contribution demonstrating proof-of-concept training efficiency and output quality, with no peer-reviewed validation, comparative benchmarks, or clinical application.
Preprint. Intervention: SCAPES model: 36-million parameter generative architecture using Continuous Normalizing Flow and Flow Matching on neural audio codec latent manifold, trained on limited uncurated datasets..
A 36-million parameter model achieves training on limited, uncurated datasets using a single consumer-grade GPU Model convergence achieved after training for approximately twice the source audio duration Outputs demonstrate high-fidelity with robust long-term stability and semantic consistency
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This is a preprint describing a novel machine learning architecture for sound synthesis with no peer review, clinical validation, or comparative efficacy data against established methods.
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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 decomposes audio into overlapping segments, enabling a Continuous Normalizing Flow (CNF) to model the evolution of latent trajectories using Flow Matching. Our experiments demonstrate that a 36-million parameter instance of SCAPES can be trained on limited, uncurated datasets using a single consumer-grade GPU. Notably, convergence is achieved after training for approximately twice the source audio duration, yielding high-fidelity outputs with robust long-term stability and semantic consistency. Furthermore, we showcase the model's capacity for smooth semantic interpolation, providing a flexible and accessible tool for open research and creative sound design. Code, pretrained weights, audio examples, and an interactive demo are publicly available on our project page https://cordutie.github.io/projects/scapes.html
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