SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Sounds

• URL: http://arxiv.org/abs/2609.04634v1
• Date: Fri, 04 Sep 2026 02:11:46 GMT
• Title: SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Sounds
• Abstract summary: We present SCAPES: a Semantically Conditioned Autoregressive Prior for Environmental Sounds.<n>SCAPES is a lightweight, resource-efficient generative model designed to synthesize high-fidelity environmental textures.<n>Code, pretrained weights, audio examples, and an interactive demo are publicly available on our project page.
• Score: 8.025248265027669
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

Related papers

• A Human-Inspired Decoupled Architecture for Efficient Audio Representation Learning [0.0]
  We propose HEAR (Human-inspired Efficient Audio Representation), a novel decoupled architecture.<n>HEAR splits the processing pipeline into two dedicated modules: an Acoustic Model for local feature extraction and a Task Model for global semantic integration.<n>Experiments demonstrate that HEAR requires only 15M parameters and 9.47 GFLOPs for inference, operating at a fraction of the computational cost of conventional foundation models.
  arXiv  Detail & Related papers  (2026-03-27T06:09:03Z)
• DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting [59.868414584142336]
  DropoutTS is a model-agnostic plugin that shifts the paradigm from "what" to "how much" to learn.<n>It maps noise to adaptive dropout rates - selectively suppressing spurious fluctuations while preserving fine-grained fidelity.
  arXiv  Detail & Related papers  (2026-01-29T13:49:20Z)
• Representation-Regularized Convolutional Audio Transformer for Audio Understanding [53.092757178419355]
  bootstrapping representations from scratch is computationally expensive, often requiring extensive training to converge.<n>We propose the Convolutional Audio Transformer (CAT), a unified framework designed to address these challenges.
  arXiv  Detail & Related papers  (2026-01-29T12:16:19Z)
• Next Tokens Denoising for Speech Synthesis [51.320443764269726]
  Dragon-FM is a novel text-to-speech (TTS) design that unifies AR and flow-matching.<n>It processes 48 kHz audio tokens in chunks at a compact rate of 12.5 tokens per second.<n>Experiments on podcast datasets demonstrate its capability to efficiently generate high-quality zero-shot podcasts.
  arXiv  Detail & Related papers  (2025-07-30T15:03:36Z)
• CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models [74.80386066714229]
  We present an improved streaming speech synthesis model, CosyVoice 2.<n>Specifically, we introduce finite-scalar quantization to improve codebook utilization of speech tokens.<n>We develop a chunk-aware causal flow matching model to support various synthesis scenarios.
  arXiv  Detail & Related papers  (2024-12-13T12:59:39Z)
• VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment [101.2489492032816]
  VALL-E R is a robust and efficient zero-shot Text-to-Speech system. This research has the potential to be applied to meaningful projects, including the creation of speech for those affected by aphasia.
  arXiv  Detail & Related papers  (2024-06-12T04:09:44Z)
• Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model [35.171785986428425]
  We propose Audio-Visual Lightweight ITerative model (AVLIT) to perform audio-visual speech separation in noisy environments. Our architecture consists of an audio branch and a video branch, with iterative A-FRCNN blocks sharing weights for each modality. Experiments demonstrate the superiority of our model in both settings with respect to various audio-only and audio-visual baselines.
  arXiv  Detail & Related papers  (2023-05-31T20:09:50Z)
• High Fidelity Neural Audio Compression [92.4812002532009]
  We introduce a state-of-the-art real-time, high-fidelity, audio leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary.
  arXiv  Detail & Related papers  (2022-10-24T17:52:02Z)
• Streamable Neural Audio Synthesis With Non-Causal Convolutions [1.8275108630751844]
  We introduce a new method allowing to produce non-causal streaming models. This allows to make any convolutional model compatible with real-time buffer-based processing. We show how our method can be adapted to fit complex architectures with parallel branches.
  arXiv  Detail & Related papers  (2022-04-14T16:00:32Z)
• RAVE: A variational autoencoder for fast and high-quality neural audio synthesis [2.28438857884398]
  We introduce a Realtime Audio Variational autoEncoder (RAVE) allowing both fast and high-quality audio waveform synthesis. We show that our model is the first able to generate 48kHz audio signals, while simultaneously running 20 times faster than real-time on a standard laptop CPU.
  arXiv  Detail & Related papers  (2021-11-09T09:07:30Z)
• MTCRNN: A multi-scale RNN for directed audio texture synthesis [0.0]
  We introduce a novel modelling approach for textures, combining recurrent neural networks trained at different levels of abstraction with a conditioning strategy that allows for user-directed synthesis. We demonstrate the model's performance on a variety of datasets, examine its performance on various metrics, and discuss some potential applications.
  arXiv  Detail & Related papers  (2020-11-25T09:13:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.