Separate Anything You Describe

• URL: http://arxiv.org/abs/2308.05037v2
• Date: Fri, 27 Oct 2023 15:34:06 GMT
• Title: Separate Anything You Describe
• Authors: Xubo Liu, Qiuqiang Kong, Yan Zhao, Haohe Liu, Yi Yuan, Yuzhuo Liu, Rui Xia, Yuxuan Wang, Mark D. Plumbley, Wenwu Wang
• Abstract summary: Language-queried audio source separation (LASS) is a new paradigm for computational auditory scene analysis (CASA) AudioSep is a foundation model for open-domain audio source separation with natural language queries.
• Score: 55.0784713558149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language-queried audio source separation (LASS) is a new paradigm for computational auditory scene analysis (CASA). LASS aims to separate a target sound from an audio mixture given a natural language query, which provides a natural and scalable interface for digital audio applications. Recent works on LASS, despite attaining promising separation performance on specific sources (e.g., musical instruments, limited classes of audio events), are unable to separate audio concepts in the open domain. In this work, we introduce AudioSep, a foundation model for open-domain audio source separation with natural language queries. We train AudioSep on large-scale multimodal datasets and extensively evaluate its capabilities on numerous tasks including audio event separation, musical instrument separation, and speech enhancement. AudioSep demonstrates strong separation performance and impressive zero-shot generalization ability using audio captions or text labels as queries, substantially outperforming previous audio-queried and language-queried sound separation models. For reproducibility of this work, we will release the source code, evaluation benchmark and pre-trained model at: https://github.com/Audio-AGI/AudioSep.

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