Biology subjects

Borjigin, A.

Publications and source records attributed to Borjigin, A..

4 recordsLinked to original sources

bioRxiv2023

Individual Differences Reveal the Utility of Temporal Fine-Structure Processing for Speech Perception in Noise

The auditory system is unique among sensory systems in its ability to phase lock to and precisely follow very fast cycle-by-cycle fluctuations in the phase of sound-driven cochlear vibrations. Yet, the perceptual role of this temporal fine structure (TFS) code is debated. This fundamental gap is attributable to our inability to experimentally manipulate TFS cues without altering other perceptually relevant cues. Here, we circumnavigated this limitation by leveraging individual differences across 200 participants to systematically compare variations in TFS sensitivity to performance in a range of speech perception tasks. TFS sensitivity was assessed through detection of interaural time/phase differences, while speech perception was evaluated by word identification under noise interference. Results suggest that greater TFS sensitivity is not associated with greater masking release from fundamental-frequency or spatial cues, but appears to contribute to resilience against the effects of reverberation. We also found that greater TFS sensitivity is associated with faster response times, indicating reduced listening effort. These findings highlight the perceptual significance of TFS coding for everyday hearing. Significance StatementNeural phase-locking to fast temporal fluctuations in sounds-temporal fine structure (TFS) in particular- is a unique mechanism by which acoustic information is encoded by the auditory system. However, despite decades of intensive research, the perceptual relevance of this metabolically expensive mechanism, especially in challenging listening settings, is debated. Here, we leveraged an individual-difference approach to circumnavigate the limitations plaguing conventional approaches and found that robust TFS sensitivity is associated with greater resilience against the effects of reverberation and is associated with reduced listening effort for speech understanding in noise.

neuroscience↗

bioRxiv2022

Deep neural network algorithms for noise reduction and their application to cochlear implants

Cochlear implants (CIs) fail to provide the same level of benefit in noisy settings as in quiet. Current noise reduction solutions in hearing aids and CIs only remove predictable, stationary noise, and are ineffective against realistic, non-stationary noise such as multi-talker interference. Recent developments in deep neural network (DNN) models have achieved noteworthy performance in speech enhancement and separation. However, little work has investigated the potential of DNN models in removing multi-talker interference. The research in this regard is even more scarce for CIs. Here, we implemented two DNN models that are well suited for applications in speech audio processing, including (1) recurrent neural network (RNN) and (2) SepFormer. The models were trained with a dataset developed in house ([~] 30 hours), and then tested with thirteen CI listeners. Both RNN and SepFormer models significantly improved CI listeners speech intelligibility in noise without compromising the perceived quality of speech. These models not only increased the intelligibility in stationary non-speech noise, but also introduced substantially more improvements in non-stationary speech noise, where conventional signal processing strategies fall short with little benefits. These results show the promise of using DNN models as a solution for listening challenges in multi-talker noise interference.

neuroscience↗

bioRxiv2021

Individualized Assays of Temporal Coding in the Ascending Human Auditory System

Neural phase-locking to temporal fluctuations is a fundamental and unique mechanism by which acoustic information is encoded by the auditory system. The perceptual role of this metabolically expensive mechanism, the neural phase-locking to temporal fine structure (TFS) in particular, is debated. Although hypothesized, it is unclear if auditory perceptual deficits in certain clinical populations are attributable to deficits in TFS coding. Efforts to uncover the role of TFS have been impeded by the fact that there are no established assays for quantifying the fidelity of TFS coding at the individual level. While many candidates have been proposed, for an assay to be useful, it should not only intrinsically depend on TFS coding, but should also have the property that individual differences in the assay reflect TFS coding per se over and beyond other sources of variance. Here, we evaluate a range of behavioral and electroencephalogram (EEG)-based measures as candidate individualized measures of TFS sensitivity. Our comparisons of behavioral and EEG-based metrics suggest that extraneous variables dominate both behavioral scores and EEG amplitude metrics, rendering them ineffective. After adjusting behavioral scores using lapse rates, and extracting latency or percent-growth metrics from EEG, interaural timing sensitivity measures exhibit robust behavior-EEG correlations. Together with the fact that unambiguous theoretical links can be made relating binaural measures and phase-locking to TFS, our results suggest that these "adjusted" binaural assays may be well-suited for quantifying individual TFS processing.

neuroscience↗

bioRxiv2021

Web-based Psychoacoustics: Hearing Screening, Infrastructure, and Validation

Anonymous web-based experiments are increasingly and successfully used in many domains of behavioral research. However, online studies of auditory perception, especially of psychoacoustic phenomena pertaining to low-level sensory processing, are challenging because of limited available control of the acoustics, and the unknown hearing status of participants. Here, we outline our approach to mitigate these challenges and validate our procedures by comparing web-based measurements to labbased data on a range of classic psychoacoustic tasks. Individual tasks were created using jsPsych, an open-source javascript front-end library. Dynamic sequences of psychoacoustic tasks were implemented using Django, an open-source library for web applications, and combined with consent pages, questionnaires, and debriefing pages. Subjects were recruited via Prolific, a web-based human-subject marketplace. Guided by a meta-analysis of normative data, we developed and validated a screening procedure to select participants for (putative) normal-hearing status; this procedure combined thresholding of scores in a suprathreshold cocktail-party task with filtering based on survey responses. Headphone use was standardized by supplementing procedures from prior literature with a binaural hearing task. Individuals meeting all criteria were re-invited to complete a range of classic psychoacoustic tasks. Performance trends observed in re-invited participants were in excellent agreement with lab-based data for fundamental frequency discrimination, gap detection, sensitivity to interaural time delay and level difference, comodulation masking release, word identification, and consonant confusions. Our results suggest that web-based psychoacoustics is a viable complement to lab-based research. Source code for our infrastructure is also provided.

neuroscience↗