A feature-based linear regression model for predicting perceptual ratings of music by cochlear implant listeners
Anil M. Nagathil, Jan-Willem Schlattmann, Katrin Neumann, Rainer Martin
Abstract
While speech quality and intelligibility prediction methods for normal-hearing and hearing-impaired listeners have found a lot of attention as a cost-saving complement to listening tests, analogous procedures for music signals are still rare. In this paper a method is proposed for predicting perceptual ratings of music as obtained by cochlear implant (CI) listeners. For this purpose a listening test with CI listeners was conducted, who were asked to provide their ratings for music excerpts on different scales. It is shown that principal component regression (PCR) is a suitable tool to model and accurately predict the median ratings of the CI listeners using timbre and pitch related signal features as predictor variables. These features describe signal characteristics such as high-frequency energy, spectral bandwidth and roughness. The proposed prediction model is a first step towards an instrumental evaluation procedure for music processing algorithms in hearing devices.
BibTeX
@inproceedings{icassp2017_afeaturebasedlin,
title = {A feature-based linear regression model for predicting perceptual ratings of music by cochlear implant listeners},
author = {Anil M. Nagathil and Jan-Willem Schlattmann and Katrin Neumann and Rainer Martin},
booktitle = {ICASSP 2017},
year = {2017}
}