Coding Strategies for Cochlear Implants Under Adverse Environments

Cochlear implants are electronic prosthetic devices that restores partial hearing in patients with severe to profound hearing loss. Although most coding strategies have significantly improved the perception of speech in quite listening conditions, there remains limitations on speech perception under adverse environments such as in background noise, reverberation and band-limited channels, and we propose strategies that improve the intelligibility of speech transmitted over the telephone networks, reverberated speech and speech in the presence of background noise. For telephone processed speech, we propose to examine the effects of adding low-frequency and high- frequency information to the band-limited telephone speech. Four listening conditions were designed to simulate the receiving frequency characteristics of telephone handsets. Results indicated improvement in cochlear implant and bimodal listening when telephone speech was augmented with high frequency information and therefore this study provides support for design of algorithms to extend the bandwidth towards higher frequencies. The results also indicated added benefit from hearing aids for bimodal listeners in all four types of listening conditions. Speech understanding in acoustically reverberant environments is always a difficult task for hearing impaired listeners. Reverberated sounds consists of direct sound, early reflections and late reflections. Late reflections are known to be detrimental to speech intelligibility. In this study, we propose a reverberation suppression strategy based on spectral subtraction to suppress the reverberant energies from late reflections. Results from listening tests for two reverberant conditions (RT60 = 0.3s and 1.0s) indicated significant improvement when stimuli was processed with SS strategy. The proposed strategy operates with little to no prior information on the signal and the room characteristics and therefore, can potentially be implemented in real-time CI speech processors. For speech in background noise, we propose a mechanism underlying the contribution of harmonics to the benefit of electroacoustic stimulations in cochlear implants. The proposed strategy is based on harmonic modeling and uses synthesis driven approach to synthesize the harmonics in voiced segments of speech. Based on objective measures, results indicated improvement in speech quality. This study warrants further work into development of algorithms to regenerate harmonics of voiced segments in the presence of noise

Localization in Reverberation with Cochlear Implants

Users of bilateral cochlear implants (CIs) experience difficulties localizing sounds in reverberant rooms, even in rooms where normal-hearing listeners would hardly notice the reverberation. We measured the localization ability of seven bilateral CI users listening with their own devices in anechoic space and in a simulated reverberant room. To determine factors affecting performance in reverberant space we measured the sensitivity to interaural time differences (ITDs), interaural level differences (ILDs), and forward masking in the same participants using direct computer control of the electric stimulation in their CIs. Localization performance, quantified by the coefficient of determination r(2) and the root mean squared error, was significantly worse in the reverberant room than in anechoic conditions. Localization performance in the anechoic room, expressed as r(2), was best predicted by subject’s sensitivity to ILDs. However, the decrease in localization performance caused by reverberation was better predicted by the sensitivity to envelope ITDs measured on single electrode pairs, with a correlation coefficient of 0.92. The CI users who were highly sensitive to envelope ITDs also better maintained their localization ability in reverberant space. Results in the forward masking task added only marginally to the predictions of localization performance in both environments. The results indicate that envelope ITDs provided by CI processors support localization in reverberant space. Thus, methods that improve perceptual access to envelope ITDs could help improve localization with bilateral CIs in everyday listening situations

Objective Assessment of Machine Learning Algorithms for Speech Enhancement in Hearing Aids

Speech enhancement in assistive hearing devices has been an area of research for many decades. Noise reduction is particularly challenging because of the wide variety of noise sources and the non-stationarity of speech and noise. Digital signal processing (DSP) algorithms deployed in modern hearing aids for noise reduction rely on certain assumptions on the statistical properties of undesired signals. This could be disadvantageous in accurate estimation of different noise types, which subsequently leads to suboptimal noise reduction. In this research, a relatively unexplored technique based on deep learning, i.e. Recurrent Neural Network (RNN), is used to perform noise reduction and dereverberation for assisting hearing-impaired listeners. For noise reduction, the performance of the deep learning model was evaluated objectively and compared with that of open Master Hearing Aid (openMHA), a conventional signal processing based framework, and a Deep Neural Network (DNN) based model. It was found that the RNN model can suppress noise and improve speech understanding better than the conventional hearing aid noise reduction algorithm and the DNN model. The same RNN model was shown to reduce reverberation components with proper training. A real-time implementation of the deep learning model is also discussed

Communication Biophysics

Contains reports on six research projects.National Institutes of Health (Grant 5 PO1 NS13126)National Institutes of Health (Grant 5 RO1 NS18682)National Institutes of Health (Grant 5 RO1 NS20322)National Institutes of Health (Grant 5 R01 NS20269)National Institutes of Health (Grant 5 T32NS 07047)Symbion, Inc.National Science Foundation (Grant BNS 83-19874)National Science Foundation (Grant BNS 83-19887)National Institutes of Health (Grant 6 RO1 NS 12846)National Institutes of Health (Grant 1 RO1 NS 21322

Physiology, Psychoacoustics and Cognition in Normal and Impaired Hearing

​The International Symposium on Hearing is a prestigious, triennial gathering where world-class scientists present and discuss the most recent advances in the field of human and animal hearing research. The 2015 edition will particularly focus on integrative approaches linking physiological, psychophysical and cognitive aspects of normal and impaired hearing. Like previous editions, the proceedings will contain about 50 chapters ranging from basic to applied research, and of interest to neuroscientists, psychologists, audiologists, engineers, otolaryngologists, and artificial intelligence researchers.

Physiology, Psychoacoustics and Cognition in Normal and Impaired Hearing

otorhinolaryngology; neurosciences; hearin