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Natural speech is organized according to a hierarchical structure, with individual speech sounds combining to form abstract linguistic units, and abstract linguistic units combining to form higher-order linguistic units. Since the boundaries between these units are not always indicated by acoustic cues, they must often be computed internally. Signatures of this internal computation were reported by Ding et al. (2016), who presented isochronous sequences of mono-syllabic words that combined to form phrases that combined to form sentences, and showed that cortical responses simultaneously encode boundaries at multiple levels of the linguistic hierarchy. In the present study, we designed melodic sequences that were hierarchically organized according to Western music conventions. Specifically, isochronous sequences of "sung" nonsense syllables were constructed such that syllables combined to form triads outlining individual chords, which combined to form harmonic progressions. EEG recordings were made while participants listened to these sequences with the instruction to detect when violations in the sequence structure occurred. We show that cortical responses simultaneously encode boundaries at multiple levels of a melodic hierarchy, suggesting that the encoding of hierarchical structure is not unique to speech. No effect of musical training on cortical encoding was observed., (Copyright © 2019 Elsevier Inc. All rights reserved.)

While wide dynamic range compression (WDRC) is a standard feature of modern hearing aids, it can be difficult to fit compression settings to individual hearing aid users. The goal of the current study was to develop a practical test to learn the preference of individual listeners for different compression ratio (CR) settings in different listening conditions (speech-in-quiet and speech-in-noise). While it is possible to exhaustively test different CR settings, such methods can take many hours to complete, making them impractical. Bayesian optimization methods were used to find CR preferences in individual listeners in a relatively short amount of time. Using this practical preference learning test, individual differences in CR preference were examined across a relatively wide range of CR settings in different listening conditions. In experiment 1, the accuracy of the preference learning test in normal hearing listeners was verified. In experiment 2, it is shown that individual hearing impaired listeners differ in their CR preferences, and listeners tended to prefer the CR setting identified by the preference learning test over both linear gain or the National Acoustics Lab--Nonlinear 2 CR prescription based on their audiograms.

Objective: To use cortical auditory evoked potentials (CAEPs) to understand neural encoding in background noise and the conditions under which noise enhances CAEP responses., Methods: CAEPs from 16 normal-hearing listeners were recorded using the speech syllable/ba/presented in quiet and speech-shaped noise at signal-to-noise ratios of 10 and 30dB. The syllable was presented binaurally and monaurally at two presentation rates., Results: The amplitudes of N1 and N2 peaks were often significantly enhanced in the presence of low-level background noise relative to quiet conditions, while P1 and P2 amplitudes were consistently reduced in noise. P1 and P2 amplitudes were significantly larger during binaural compared to monaural presentations, while N1 and N2 peaks were similar between binaural and monaural conditions., Conclusions: Methodological choices impact CAEP peaks in very different ways. Negative peaks can be enhanced by background noise in certain conditions, while positive peaks are generally enhanced by binaural presentations., Significance: Methodological choices significantly impact CAEPs acquired in quiet and in noise. If CAEPs are to be used as a tool to explore signal encoding in noise, scientists must be cognizant of how differences in acquisition and processing protocols selectively shape CAEP responses., (Published by Elsevier Ireland Ltd.)

Objective: The purpose of this study was to determine the effects of SNR and signal level on the offset response of the cortical auditory evoked potential (CAEP). Successful listening often depends on how well the auditory system can extract target signals from competing background noise. Both signal onsets and offsets are encoded neurally and contribute to successful listening in noise. Neural onset responses to signals in noise demonstrate a strong sensitivity to signal-to-noise ratio (SNR) rather than signal level; however, the sensitivity of neural offset responses to these cues is not known., Methods: We analyzed the offset response from two previously published datasets for which only the onset response was reported. For both datasets, CAEPs were recorded from young normal-hearing adults in response to a 1000-Hz tone. For the first dataset, tones were presented at seven different signal levels without background noise, while the second dataset varied both signal level and SNR., Results: Offset responses demonstrated sensitivity to absolute signal level in quiet, SNR, and to absolute signal level in noise., Conclusions: Offset sensitivity to signal level when presented in noise contrasts with previously published onset results., Significance: This sensitivity suggests a potential clinical measure of cortical encoding of signal level in noise.