Despite advances in cochlear implant (CI) technology, music enjoyment remains a significant challenge for most CI users. To facilitate music listening in CI-mediated hearing, music processing algorithms have been proposed, which reduce the perceived complexity of music pieces. As these algorithms rely on a multitude of signal processing parameters, finding suitable and possibly genre-specific settings can be a tedious process. Therefore, in this study, we propose and evaluate an interactive optimization scheme for parametric music processing algorithms. To minimize the burden on CI users, the process was divided into two phases: first, normal-hearing (NH) participants evaluated a wide range of random parameter combinations using a vocoder-based CI simulation. Then, CI users participated in shorter, focused optimization sessions to refine these parameters. For comparison, we also conducted parallel listening sessions with NH listeners using the vocoder simulation. We tested the approach using a parametric music remixing algorithm that had previously been validated with a hand-crafted parameter setting found through expert knowledge. Starting from completely randomized initial settings, the method was able to find optimized parameter values through interactive user feedback. Both groups, CI listeners and NH listeners using a CI simulation, significantly preferred the optimized remixes over unprocessed music, while for CI listeners, the preference for the optimized settings was on par with the hand-crafted setting. NH listeners using the CI simulation even significantly preferred their optimized remixes. The results of this study demonstrate the utility of the proposed optimization scheme and pave the way for time-efficient, interactive tuning of future music processing algorithms.
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