A Reference-free Metric for Language-Queried Audio Source Separation using Contrastive Language-Audio Pretraining

Understand

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.