A computational study of auditory models in music recognition tasks for normal-hearing and hearing-impaired listeners

Abstract

The benefit of auditory models for solving three music recognition tasks—onset detection, pitch estimation, and instrument recognition—is analyzed. Appropriate features are introduced which enable the use of supervised classification. The auditory model-based approaches are tested in a comprehensive study and compared to state-of-the-art methods, which usually do not employ an auditory model. For this study, music data is selected according to an experimental design, which enables statements about performance differences with respect to specific music characteristics. The results confirm that the performance of music classification using the auditory model is comparable to the traditional methods. Furthermore, the auditory model is modified to exemplify the decrease of recognition rates in the presence of hearing deficits. The resulting system is a basis for estimating the intelligibility of music which in the future might be used for the automatic assessment of hearing instruments.