  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. Experimental results show that the CLAPScore metric
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.
