Speech intelligibility in noise varies widely across hearing aid users despite noise management features. Traditional approaches, such as adaptive beamforming and single-channel noise reduction, can reduce background noise but may introduce signal distortions that degrade speech intelligibility, particularly for listeners with poorer cognitive abilities. Newer deep neural network-based noise reduction (DNN) can potentially improve signal-to-noise ratio while minimizing distortions. This study aims to characterize how individual cognitive abilities influence outcomes with DNN relative to adaptive beamforming across acoustic scenes. Listeners with hearing loss completed sentence intelligibility tasks using wearable hearing aids programmed with adaptive beamforming and DNN. Target speech and competing maskers were presented from on- and off-axis locations in co-located and spatially separated configurations, with masker types including gender-matched two-talker speech or diffuse noise. Participants also completed a cognitive test battery assessing working memory, processing speed, and attention control. Results-to-date indicate that on average beamforming results in higher intelligibility with two-talker maskers, whereas DNN provides better intelligibility in diffuse noise and for off-axis targets, with large individual variability. The presentation will discuss how variability in intelligibility across these noise management strategies relates to individual cognitive profiles and susceptibility to signal distortions.
Varsha Rallapalli, Sophia Kreismer, Erol J. Ozmeral· Journal of the Acoustical So...· 0 citations
The purpose of this study was to compare the effects of source-specific (independent) and conventional dynamic range compression (DRC) on sound quality ratings among listeners with hearing loss when ground-truth signals are available to the compressor. Twenty listeners with mild to moderately severe sensorineural hearing loss rated the sound quality on different subscales for two types of signal mixtures: speech in music (Overall Sound Quality, Speech Clarity, and Music Pleasantness) and speech in noise (Overall Sound Quality, Speech Clarity, and Noisiness) at three speech-to-background ratios (SBR: -10, 0, +10 dB). Speech in music was a 10-second-long spontaneous speech excerpt with a duration-matched classical music excerpt. Speech in noise had the same speech signal mixed with speech-shaped noise. Conventional DRC applied a 50-ms release time to the mixed signals, whereas independent DRC applied a 50-ms release time for speech and a 2000-ms release time for music or noise before mixing. The control conditions included linear amplification applied to the signals before and after mixing. Independent DRC resulted in higher overall sound quality, music pleasantness, and lower noisiness ratings than conventional DRC, regardless of SBR. Independent DRC resulted in higher speech clarity ratings than conventional DRC at lower SBRs. The results are generally supported by acoustic metrics, indicating more effective compression and higher output SBRs, especially at lower input SBRs, and reduced across-source modulation correlations with independent DRC. These findings extend the literature on the advantages of independent compression of sound sources by demonstrating sound quality benefits across multiple sub-scales for listeners with hearing loss across music and noise backgrounds.
Varsha Rallapalli, Catherine Steinwachs, R. Corey· Trends in Hearing· 0 citations
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