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

Koklear İmplant Konuşma İşlemcileri için Optimum Parametrelerin Objektif Ölçütler Kullanılarak Belirlenmesi

In a cochlear implant (CI) speech processor, several parameters such as channel numbers, bandwidths, rectification type, and cutoff frequency play an important role in acquiring enhanced speech. The effective and general purpose CI approach has been a research topic for a long time. In this study, it is aimed to determine the optimum parameters for CI users by using different channel numbers (4, 8, 12, 16, and 22), rectification types (half and full) and cutoff frequencies (200, 250, 300, 350, and 400 Hz). The CI approaches have been tested on Turkish sentences which are taken from METU database. The optimum CI structure has been tested with objective quality that weighted spectral slope (WSS) and objective intelligibility measures such as short-term objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ). Experimental results show that 400 Hz cutoff frequency, full wave rectifier, and 16-channels CI approach give better quality and higher intelligibility scores than other CI approaches according to STOI, PESQ and WSS results. The proposed CI approach provides the ability to percept 91% of output vocoded Turkish speech for CI users. © 2022, TUBITAK. All rights reserved

Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants.

Speech-in-noise perception is a major problem for users of cochlear implants (CIs), especially with non-stationary background noise. Noise-reduction algorithms have produced benefits but relied on a priori information about the target speaker and/or background noise. A recurrent neural network (RNN) algorithm was developed for enhancing speech in non-stationary noise and its benefits were evaluated for speech perception, using both objective measures and experiments with CI simulations and CI users. The RNN was trained using speech from many talkers mixed with multi-talker or traffic noise recordings. Its performance was evaluated using speech from an unseen talker mixed with different noise recordings of the same class, either babble or traffic noise. Objective measures indicated benefits of using a recurrent over a feed-forward architecture, and predicted better speech intelligibility with than without the processing. The experimental results showed significantly improved intelligibility of speech in babble noise but not in traffic noise. CI subjects rated the processed stimuli as significantly better in terms of speech distortions, noise intrusiveness, and overall quality than unprocessed stimuli for both babble and traffic noise. These results extend previous findings for CI users to mostly unseen acoustic conditions with non-stationary noise

Deep learning-based denoising streamed from mobile phones improves speech-in-noise understanding for hearing aid users

The hearing loss of almost half a billion people is commonly treated with hearing aids. However, current hearing aids often do not work well in real-world noisy environments. We present a deep learning based denoising system that runs in real time on iPhone 7 and Samsung Galaxy S10 (25ms algorithmic latency). The denoised audio is streamed to the hearing aid, resulting in a total delay of around 75ms. In tests with hearing aid users having moderate to severe hearing loss, our denoising system improves audio across three tests: 1) listening for subjective audio ratings, 2) listening for objective speech intelligibility, and 3) live conversations in a noisy environment for subjective ratings. Subjective ratings increase by more than 40%, for both the listening test and the live conversation compared to a fitted hearing aid as a baseline. Speech reception thresholds, measuring speech understanding in noise, improve by 1.6 dB SRT. Ours is the first denoising system that is implemented on a mobile device, streamed directly to users' hearing aids using only a single channel as audio input while improving user satisfaction on all tested aspects, including speech intelligibility. This includes overall preference of the denoised and streamed signal over the hearing aid, thereby accepting the higher latency for the significant improvement in speech understanding

A non-intrusive method for estimating binaural speech intelligibility from noise-corrupted signals captured by a pair of microphones

A non-intrusive method is introduced to predict binaural speech intelligibility in noise directly from signals captured using a pair of microphones. The approach combines signal processing techniques in blind source separation and localisation, with an intrusive objective intelligibility measure (OIM). Therefore, unlike classic intrusive OIMs, this method does not require a clean reference speech signal and knowing the location of the sources to operate. The proposed approach is able to estimate intelligibility in stationary and fluctuating noises, when the noise masker is presented as a point or diffused source, and is spatially separated from the target speech source on a horizontal plane. The performance of the proposed method was evaluated in two rooms. When predicting subjective intelligibility measured as word recognition rate, this method showed reasonable predictive accuracy with correlation coefficients above 0.82, which is comparable to that of a reference intrusive OIM in most of the conditions. The proposed approach offers a solution for fast binaural intelligibility prediction, and therefore has practical potential to be deployed in situations where on-site speech intelligibility is a concern

Electroacoustic Assessment of Hearing Aids and PSAPs

Hearing aids and personal sound amplification products (PSAPs) are commonly used assistive devices for treating hearing loss. Due to the diversity in the hardware and signal processing algorithms in these devices, comprehensive verification of their performance is essential. Existing standards for assistive hearing devices are primarily used for quality control purposes and do not quantify their performance in a perceptually-relevant manner. This thesis developed a comprehensive electroacoustic testing toolbox for hearing devices that encompasses both quality control and perceptually-relevant measures. In particular, a test sequence was developed to assess the effectiveness of noise reduction feature in assistive hearing devices. Several commercially-available hearing aids and PSAPs on the “best seller” list at Amazon.ca were evaluated using the toolbox. Key results include: (a) hearing aids differ in their noise reduction performance; (b) some of the popular PSAPs do not meet the ANSI standards and are capable of producing dangerous sound pressure levels; and (c) hearing aids performed better than PSAPs on perceptually-relevant metrics