Cochlear implant (CI) users have traditionally reported less enjoyment and have performed more poorly on tasks of music perception (timbre, melody and pitch) than their normal hearing (NH) counterparts. The enjoyment and perception of music can be affected by the MAP programmed into a user’s speech processor, the parameters of which can be altered to change the way that a CI recipient hears sound. However, finding the optimal MAP can prove challenging to clinicians because altering one parameter will affect others.

 Until recently the only way to find the optimal MAP has theoretically been to present each

potential combination of parameters systematically, however this is impractical in a clinical setting due to the thousands of different potential combinations. Thus, in general, clinicians can find a good MAP, but not necessarily the best one. The goal of this study was to assess whether a Genetic Algorithm would assist clinicians to create a better MAP for music listening than current methods.

 Seven adult Nucleus Freedom CI users were assessed on tasks of timbre identification, melody identification and pitch-ranking using their original MAP. The participants then used the GA software to create an individualised MAP for music listening (referred to as their “GA MAP”). They then spent four weeks comparing their GA and original MAPs in their everyday life, and recording their listening experiences in a listening diary. At the end of this period participants were assessed on the same timbre, melody, and pitch tasks using their GA MAP.

 The results of the study showed that the GA process took an average of 35 minutes (range: 13-72 minutes) to create a MAP for music listening. As a group, participants reported the GA MAP to be slightly better than their original MAP for music listening, and preferred the GA MAP when at the cinema. Participants, on average, also performed significantly better on the melody identification task with their GA MAP; however they were significantly better on the half-octave interval pitch ranking task with their original MAP. The results also showed that participants were significantly more accurate on the single-instrument identification task than the ensemble instrument identification task regardless of which MAP they used. Overall, the results show that a GA can be used to successfully create a MAP for music listening, with two participants creating a MAP that they decided to keep at the conclusion of the study.