A computationally efficient music processing algorithm for cochlear implants based on an auditory adaptation model

Listening to music is a major challenge for most cochlear implant (CI) users because of device limitations and physiological constraints that degrade musical cues and lead to increased listening effort. Music pre-processing can enhance appreciation, but existing algorithms are computationally too demanding for real-time implementation in current CIs. This work proposes a lightweight algorithm based on an auditory adaptation model that attenuates slowly varying components while preserving salient elements such as vocals and drums. In listening experiments with ten CI users, the method was significantly preferred over unprocessed music and was on par with a more computationally demanding reference approach.