A comparative study of sound sources separation by independent component analysis and binaural model

Abstract

Humans’ auditory system can separate mixed sounds based on their sources easily. However, mimicking this ability by computer algorithm is not an easy task. Some approaches have been developed, particularly based on the statistical approach and binaural modeling. From statistical methods, independent component analysis (ICA) grows fast to mimics sound separation and localization by human auditory processing. On the other side, mathematical modeling to model binaural hearing has been built block by block. This paper is a comparative study of both approaches, a statistical method represented by FastICA and binaural modeling represented by the frequency domain binaural model. The task is to mimic how to binaural processing works to separate sound sources. The result of the comparison was given by the perceptual evaluation of speech quality (PESQ) and Itakura-Saito (IS) distortion measurement. PESQ scores ICA method obtains better performance than the binaural model while, in contrast, IS scores the binaural model better than ICA.