Improved Perceptual Metrics for the Evaluation of Audio Source Separation

Authors

• Emmanuel Vincent

Abstract

We aim to predict the perceived quality of estimated source signals in the context of audio source separation. Recently, we proposed a set of metrics called PEASS that consist of three computation steps: decomposition of the estimation error into three components, measurement of the salience of each component via the PEMO-Q auditory-motivated measure, and combination of these saliences via a nonlinear mapping trained on subjective opinion scores. The parameters of the decomposition were shown to have little influence on the prediction performance. In this paper, we evaluate the impact of the parameters of PEMO-Q and the nonlinear mapping on the prediction performance. By selecting the optimal parameters, we improve the average correlation with mean opinion scores (MOS) from 0.738 to 0.909 in a cross-validation setting. The resulting improved metrics are used in the context of the 2011 Signal Separation Evaluation Campaign (SiSEC).