Using Channel-Specific Models to Detect and Mitigate Reverberation in Cochlear Implants

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Desmond, Jill Marie


Collins, Leslie M.

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Cochlear implants (CIs) are devices that restore some level of hearing to deaf individuals. Because of their design and the impaired nature of the deafened auditory system, CIs provide listeners with limited spectral and temporal information, resulting in speech recognition that degrades more rapidly for CI listeners than for normal hearing listeners in noisy and reverberant environments (Kokkinakis and Loizou, 2011). This research project aimed to mitigate the effects of reverberation by directly manipulating the CI pulse train. A reverberation detection algorithm was initially developed to control processing when switching between the mitigation algorithm and a standard signal processing algorithm used when no mitigation is needed. Next, the benefit of removing two separate effects of reverberation was studied. Finally, two reverberation mitigation algorithms were developed. Because the two algorithms resulted in comparable performance, the effect of one algorithm on speech recognition was assessed in normal hearing (NH) and CI listeners.

Reverberation detection, which has not been thoroughly investigated in the CI literature, would provide a method to control the initiation of a reverberation mitigation algorithm. Although a mitigation algorithm would ideally remove reverberation without affecting non-reverberant signals, most noise and reverberation mitigation algorithms make errors and should only be applied when necessary. Therefore, a reverberation detection algorithm was designed to control the reverberation mitigation algorithm and thereby reduce unnecessary processing. The detection algorithm was implemented by first developing features from the frequency-time matrices that result from the standard CI speech processing algorithm. Next, using these features, a maximum a posteriori classifier was shown to successfully discriminate speech in quiet, reverberation, speech shaped noise, and white Gaussian noise with 94% accuracy.

In order to develop the mitigation algorithm that would be controlled by the reverberation detection algorithm, a unique approach to reverberation mitigation was considered. This research project hypothesized that focusing mitigation on one effect of reverberation, either self-masking (masking within an individual phoneme) or overlap-masking (masking of one phoneme by a preceding phoneme) (Bolt and MacDonald, 1949), may allow for a reverberation mitigation strategy that operates in real-time. In order to determine the feasibility of this approach, the benefit of mitigating the two effects of reverberation was assessed by comparing speech recognition scores for speech in reverberation to reverberant speech after ideal self-masking mitigation and to reverberant speech after ideal overlap-masking mitigation. Testing was completed with normal hearing listeners via an acoustic model as well as with CI listeners using their devices. Mitigating either effect was found to improve CI speech recognition in reverberant environments. These results suggested that a new, causal approach could be taken to reverberation mitigation.

Based on the success of the feasibility study, two initial overlap-masking mitigation algorithms were implemented and applied once reverberation was detected in speech stimuli. One algorithm processed a pulse train signal after CI speech processing, while the second algorithm processed the acoustic signal. Performance of the two overlap-masking mitigation algorithms was evaluated in simulation by comparing pulses that were determined to be overlap-masking with the known truth. Using the features explored in this work, performance was comparable between the two methods. Therefore, only the post-CI speech processing reverberation mitigation algorithm was implemented in a CI speech processing strategy.

An initial experiment was conducted, using NH listeners and an acoustic model designed to present the frequency and temporal information that would be available to a CI listener. Listeners were presented with speech stimuli in the presence of both mitigated and unmitigated simulated reverberant conditions, and speech recognition was found to improve after reverberation mitigation. A subsequent experiment, also using NH listeners and an acoustic model, explored the effects of recorded room impulse responses (RIRs) and added noise (speech shaped noise and multi-talker babble) on the mitigation strategy. Because reverberation mitigation did not consistently improve speech recognition in these conditions, an analysis of the fundamental differences between simulated and recorded RIRs was conducted. Finally, CI listeners were presented with simulated reverberant speech, both with and without reverberation mitigation, and the effect of the mitigation strategy on speech recognition was studied. Because the reverberation mitigation strategy did not consistently improve speech recognition, future work is required to analyze the effects of algorithm-specific parameters for CI listeners.





Desmond, Jill Marie (2014). Using Channel-Specific Models to Detect and Mitigate Reverberation in Cochlear Implants. Dissertation, Duke University. Retrieved from


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