A Reference-free Metric for Language-Queried Audio Source Separation using Contrastive Language-Audio Pretraining
Abstract
Language-queried audio source separation (LASS) aims to separate an audio source guided by a text query, with the signal-to-distortion ratio (SDR)-based metrics being commonly used to objectively measure the quality of the separated audio. However, the SDR-based metrics require a reference signal, which is often difficult to obtain in real-world scenarios. In addition, with the SDR-based metrics, the content information of the text query is not considered effectively in LASS. This paper introduces a reference-free evaluation metric using a contrastive language-audio pretraining (CLAP) module, termed CLAPScore, which measures the semantic similarity between the separated audio and the text query. Unlike SDR, the proposed CLAPScore metric evaluates the quality of the separated audio based on the content information of the text query, without needing a reference signal. Experiments show that the CLAPScore provides an effective evaluation of the semantic relevance of the separated audio to the text query, as compared to the SDR metric, offering an alternative for the performance evaluation of LASS systems. The code for evaluation is publicly available.
Keywords
Cite
@article{arxiv.2407.04936,
title = {A Reference-free Metric for Language-Queried Audio Source Separation using Contrastive Language-Audio Pretraining},
author = {Feiyang Xiao and Jian Guan and Qiaoxi Zhu and Xubo Liu and Wenbo Wang and Shuhan Qi and Kejia Zhang and Jianyuan Sun and Wenwu Wang},
journal= {arXiv preprint arXiv:2407.04936},
year = {2025}
}
Comments
Accepted by DCASE 2024 Workshop. GitHub: https://github.com/LittleFlyingSheep/CLAPScore_for_LASS