Using channel-specific statistical models to detect reverberation in cochlear implant stimuli.

Reverberation is especially detrimental for cochlear implant listeners; thus, mitigating its effects has the potential to provide significant improvements to cochlear implant communication. Efforts to model and correct for reverberation in acoustic listening scenarios can be quite complex, requiring estimation of the room transfer function and localization of the source and receiver. However, due to the limited resolution associated with cochlear implant stimulation, simpler processing for reverberation detection and mitigation may be possible for cochlear implants. This study models speech stimuli in a cochlear implant on a per-channel basis both in quiet and in reverberation, and assesses the efficacy of these models for detecting the presence of reverberation. This study was able to successfully detect reverberation in cochlear implant pulse trains, and the results appear to be robust to varying room conditions and cochlear implant stimulation parameters. Reverberant signals were detected 100% of the time for a long reverberation time of 1.2 s and 86% of the time for a shorter reverberation time of 0.5 s.