Spectral Complexity Reduction of Music Signals for Mitigating Effects of Cochlear Hearing Loss
Anil Nagathil, Claus Weihs, Rainer Martin · IEEE/ACM Transactions on Audio Speech and Language Processing · 2015
In this paper we study reduced-rank approximations of music signals in the constant-Q spectral domain as a means to reduce effects stemming from cochlear hearing loss. The rationale behind computing reduced-rank approximations is that they allow to reduce the spectral complexity of a music signal. The method is motivated by studies with cochlear implant listeners which have shown that solo instrumental music or music remixed at higher signal-to-interference ratios are preferred over complex music ensembles or orchestras. For computing the reduced-rank approximations we investigate methods based on principal component analysis and partial least squares analysis, and compare them to source separation algorithms. The strategies, which are applied to music with a predominant leading voice, are compared in terms of their ability for mitigating effects of simulated reduced frequency selectivity and with respect to source signal distortions. Established instrumental measures and a newly developed measure indicate a considerable reduction of the auditory distortion resulting from cochlear hearing loss. Furthermore, a listening test reveals a significant preference for the reduced-rank approximations in terms of melody clarity and ease of listening.