Interleaved Acoustic Environments: Impact of an Auditory Scene Classification Procedure on Speech Perception in Cochlear Implant Users

Abstract

Clinical speech perception tests with simple presentation conditions often overestimate the impact of signal preprocessing on speech perception in complex listening environments. A new procedure was developed to assess speech perception in interleaved acoustic environments of different complexity that allows investigation of the impact of an automatic scene classification (ASC) algorithm on speech perception. The procedure was applied in cohorts of normal hearing (NH) controls and uni- and bilateral cochlear implant (CI) users. Speech reception thresholds (SRTs) were measured by means of a matrix sentence test in five acoustic environments that included different noise conditions (amplitude modulated and continuous), two spatial configurations, and reverberation. The acoustic environments were encapsulated in a randomized, mixed order single experimental run. Acoustic room simulation was played back with a loudspeaker auralization setup with 128 loudspeakers. 18 NH, 16 unilateral, and 16 bilateral CI users participated. SRTs were evaluated for each individual acoustic environment and as mean-SRT. Mean-SRTs improved by 2.4 dB signal-to-noise ratio for unilateral and 1.3 dB signal-to-noise ratio for bilateral CI users with activated ASC. Without ASC, the mean-SRT of bilateral CI users was 3.7 dB better than the SRT of unilateral CI users. The mean-SRT indicated significant differences, with NH group performing best and unilateral CI users performing worse with a difference of up to 13 dB compared to NH. The proposed speech test procedure successfully demonstrated that speech perception and benefit with ASC depend on the acoustic environment.

A bioinspired configurable cochlea based on memristors

“…Benefit from the multi-channel parallel processing feature, the organisms could process and perceive complex audio signals with high efficiency ( Luo, 2021 ). Inspired by the biological cochlea, artificial cochleas have been widely used in mobile devices ( Geronazzo et al, 2020 ; Zheng et al, 2021 ), smart homes ( Mondal and Barman, 2022 ; Priya et al, 2022 ), biomedical healthcare system ( Islam et al, 2022 ; Wang et al, 2022 ), and other voice interaction interfaces to perform assigned tasks ( Eichenauer et al, 2021 ; Ghosh et al, 2022 ). However, the artificial cochlear system based on complementary metal-oxide-semiconductor (CMOS) technology, with the advent of the post-Moore era, presents challenges on system complexity, energy consumption, scalability and configurability ( Xu et al, 2018 ; Wang et al, 2021 ; Ding et al, 2022 ).…”