A subjective evaluation of different music preprocessing approaches in cochlear implant listeners

Abstract

Cochlear implants (CIs) can partially restore speech perception to relatively high levels in listeners with moderate to profound hearing loss. However, for most CI listeners, the perception and enjoyment of music remains notably poor. Since a number of technical and physiological restrictions of current implant designs cannot be easily overcome, a number of preprocessing methods for music signals have been proposed recently. They aim to emphasize the leading voice and rhythmic elements and to reduce their spectral complexity. In this study, CI listeners evaluated five remixing approaches in comparison to unprocessed signals. To identify potential explaining factors of CI preference ratings, different signal quality criteria of the processed signals were additionally assessed by normal-hearing listeners. Additional factors were investigated based on instrumental signal-level features. For three preprocessing methods, a significant improvement over the unprocessed reference was found. Especially, two deep neural network-based remix strategies proved to enhance music perception in CI listeners. These strategies provide remixes of the respective harmonic and percussive signal components of the four source stems “vocals,” “bass,” “drums,” and “other accompaniment.” Moreover, the results demonstrate that CI listeners prefer an attenuation of sustained components of drum source signals.