Towards a Model of Perceived Quality of Blind Audio Source Separation

Existing perceptual models of audio quality, such as PEAQ, perform poorly when applied to blind audio source separation (BASS). We propose to create a perceptual model designed specifically for BASS algorithms. To create this model, we have designed a study to capture subjective human assessments of signal distortions resulting from BASS. In this study, humans rate the similarity between pairs of sounds. The first sound in each pair is a reference sound. The second sound is a distorted version of the reference, extracted from a multi-source mixture by a current BASS approach. We then correlate human similarity assessments with machine-measurable parameters. This paper describes preliminary results from a pilot study of three participants. Results indicate a strong correlation between human similarity assessments and the relative fraction of frames for which at last one frequency band in the distorted signal contains a significant noise component (RDF).