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 lab-based 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.

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.

Anonymous web-based experiments are increasingly 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 inability to perform audiometry to confirm normal-hearing status of participants. Here, we outline our approach to mitigate these challenges and validate our procedures by comparing web-based measurements to lab-based 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 subject recruitment platform for web-based studies. Guided by a meta-analysis of lab-based data, we developed and validated a screening procedure to select participants for (putative) normal-hearing status based on their responses in a suprathreshold task and a survey. 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. For the re-invited participants, absolute thresholds were in excellent agreement with lab-based data for fundamental frequency discrimination, gap detection, and sensitivity to interaural time delay and level difference. Furthermore, word identification scores, consonant confusion patterns, and co-modulation masking release effect also matched lab-based studies. Our results suggest that web-based psychoacoustics is a viable complement to lab-based research. Source code for our infrastructure is provided.

Despite excellent performance in quiet, cochlear implants (CIs) only partially restore normal levels of intelligibility in noisy settings. Recent developments in machine learning have resulted in deep neural network (DNN) models that achieve noteworthy performance in speech enhancement and separation tasks. However, there are no commercially available CI audio processors that utilize DNN models for noise reduction. We implemented two DNN models intended for applications in CIs: (1) a recurrent neural network (RNN), which is a lightweight template model, and (2) SepFormer, which is the current top-performing speech separation model in the literature. The models were trained with a custom training dataset (30 hours) that included four configurations: speech in non-speech noise and speech in 1-talker, 2-talker, and 4-talker speech babble backgrounds. The enhancement of the target speech (or the suppression of the noise) by the models was evaluated by commonly used acoustic evaluation metrics of quality and intelligibility, including (1) signal-to-distortion ratio, (2) ``perceptual'' evaluation of speech quality, and (3) short-time objective intelligibility. Both DNN models yielded significant improvements in all acoustic metrics tested. The two DNN models were also evaluated with thirteen CI users using two types of background noise: (1) CCITT noise (speech-shaped stationary noise) and (2) 2-talker babble. Significant improvements in speech intelligibility were observed when the noisy speech was processed by the models, compared to the unprocessed conditions. This work serves as a proof of concept for the application of DNN technology in CIs for improved listening experience and speech comprehension in noisy environments.

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.