English(EN) SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Sounds

新型SCAPES模型高效生成环境声音

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了SCAPES，一种新的、轻量级且资源高效的环境声音生成模型。该模型通过在神经音频编解码器的连续潜在流形上操作，利用高级语义控制来合成高保真环境纹理。SCAPES使用带有流匹配的连续归一化流来建模潜在轨迹，使得一个拥有3600万参数的实例可以在有限的数据集上使用单个消费级GPU进行训练。 AI

影响该模型通过降低计算和生态成本，为创意声音设计和开放研究提供了更易于访问和更灵活的工具。

排序理由该条目是一篇研究论文，详细介绍了一种新的环境声音生成模型。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Esteban Guti\'errez, Lonce Wyse, Frederic Font, Xavier Serra · 2026-09-07 04:00

SCAPES：用于环境声音的语义条件自回归先验

arXiv:2609.04634v1 Announce Type: cross 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 p…

本文如何被排名

报道来源 [1]

SCAPES：用于环境声音的语义条件自回归先验

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