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Purpose: The hearing outcomes of cochlear implant users depend on the functional status of the electrode-neuron interface inside the cochlea. This can be assessed by measuring electrically evoked compound action potentials (eCAPs). Variations in cochlear neural health and survival are reflected in eCAP-based metrics. The difficulty in translating promising results from animal studies into clinical use has raised questions about to what degree eCAP-based metrics are influenced by non-neural factors. Here, we addressed these questions using a computational model. Methods: A 2-D computational model was designed to simulate how electrical signals from the stimulating electrode reach the auditory nerve fibers distributed along the cochlea, evoking action potentials that can be recorded as compound responses at the recording electrodes. Effects of physiologically relevant variations in neural survival and in electrode-neuron and stimulating-recording electrode distances on eCAP amplitude growth functions (AGFs) were investigated. Results: In line with existing literature, the predicted eCAP AGF slopes and the inter-phase gap (IPG) effects depended on the neural survival, but only when the IPG effect was calculated as the difference between the slopes of the two AGFs expressed in linear input–output scale. As expected, shallower eCAP AGF slopes were obtained for increased stimulating-recording electrode distance and larger eCAP thresholds for greater electrode-neuron distance. These non-neural factors had also minor interference on the predicted IPG effect. Conclusions: The model predictions demonstrate previously found dependencies of eCAP metrics on neural survival and non-neural aspects. The present findings confirm data from animal studies and provide insights into applying described metrics in clinical practice. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs), which are composed of biologically plausible spiking neurons, and combined with bio-physically realistic auditory periphery models, offer a means to explore and understand human auditory processing-especially in tasks where precise timing is essential. However, because of the inherent temporal complexity in spike sequences, the performance of SNNs has remained less competitive compared to artificial neural networks (ANNs). To tackle this challenge, a fundamental research topic is the configuration of spike-timing and the exploration of more intricate architectures. In this work, we demonstrate a learnable axonal delay combined with local skip-connections yields state-of-the-art performance on challenging benchmarks for spoken word recognition. Additionally, we introduce an auxiliary loss term to further enhance accuracy and stability. Experiments on the neuromorphic speech benchmark datasets, NTIDIDIGITS and SHD, show improvements in performance when incorporating our delay module in comparison to vanilla feed forward SNNs. Specifically, with the integration of our delay module, the performance on NTIDIDIGITS and SHD improves by 14% and 18%, respectively. When paired with local skip-connections and the auxiliary loss, our approach surpasses both recurrent and convolutional neural networks, yet uses 10x fewer parameters for NTIDIDIGITS and 7x fewer for SHD. [ABSTRACT FROM AUTHOR]

Every-day acoustical environments are often complex, typically comprising one attended target sound in the presence of interfering sounds (e.g., disturbing conversations) and reverberation. Here we assessed binaural detection thresholds and (supra-threshold) binaural audio quality ratings of four distortions types: spectral ripples, non-linear saturation, intensity and spatial modifications applied to speech, guitar, and noise targets in such complex acoustic environments (CAEs). The target and (up to) two masker sounds were either co-located as if contained in a common audio stream, or were spatially separated as if originating from different sound sources. The amount of reverberation was systematically varied. Masker and reverberation had a significant effect on the distortion-detection thresholds of speech signals. Quality ratings were affected by reverberation, whereas the effect of maskers depended on the distortion. The results suggest that detection thresholds and quality ratings for distorted speech in anechoic conditions are also valid for rooms with mild reverberation, but not for moderate reverberation. Furthermore, for spectral ripples, a significant relationship between the listeners' individual detection thresholds and quality ratings was found. The current results provide baseline data for detection thresholds and audio quality ratings of different distortions of a target sound in CAEs, supporting the future development of binaural auditory models. [ABSTRACT FROM AUTHOR]

Every-day acoustical environments are often complex, typically comprising one attended target sound in the presence of interfering sounds (e.g., disturbing conversations) and reverberation. Here we assessed binaural detection thresholds and (supra-threshold) binaural audio quality ratings of four distortions types: spectral ripples, non-linear saturation, intensity and spatial modifications applied to speech, guitar, and noise targets in such complex acoustic environments (CAEs). The target and (up to) two masker sounds were either co-located as if contained in a common audio stream, or were spatially separated as if originating from different sound sources. The amount of reverberation was systematically varied. Masker and reverberation had a significant effect on the distortion-detection thresholds of speech signals. Quality ratings were affected by reverberation, whereas the effect of maskers depended on the distortion. The results suggest that detection thresholds and quality ratings for distorted speech in anechoic conditions are also valid for rooms with mild reverberation, but not for moderate reverberation. Furthermore, for spectral ripples, a significant relationship between the listeners’ individual detection thresholds and quality ratings was found. The current results provide baseline data for detection thresholds and audio quality ratings of different distortions of a target sound in CAEs, supporting the future development of binaural auditory models.

Over the past decades, different types of auditory models have been developed to study the functioning of normal and impaired auditory processing. Several models can simulate frequency-dependent sensorineural hearing loss (SNHL) and can in this way be used to develop personalized audio-signal processing for hearing aids. However, to determine individualized SNHL profiles, we rely on indirect and noninvasive markers of cochlear and auditory-nerve (AN) damage. Our progressive knowledge of the functional aspects of different SNHL subtypes stresses the importance of incorporating them into the simulated SNHL profile, but has at the same time complicated the task of accomplishing this on the basis of noninvasive markers. In particular, different auditory-evoked potential (AEP) types can show a different sensitivity to outer-hair-cell (OHC), inner-hair-cell (IHC), or AN damage, but it is not clear which AEP-derived metric is best suited to develop personalized auditory models. This study investigates how simulated and recorded AEPs can be used to derive individual AN- or OHC-damage patterns and personalize auditory processing models. First, we individualized the cochlear model parameters using common methods of frequency-specific OHC-damage quantification, after which we simulated AEPs for different degrees of AN damage. Using a classification technique, we determined the recorded AEP metric that best predicted the simulated individualized cochlear synaptopathy profiles. We cross-validated our method using the data set at hand, but also applied the trained classifier to recorded AEPs from a new cohort to illustrate the generalizability of the method. [ABSTRACT FROM AUTHOR]

Auditory perception involves cues in the monaural auditory pathways, as well as binaural cues based on interaural differences. So far, auditory models have often focused on either monaural or binaural experiments in isolation. Although binaural models typically build upon stages of (existing) monaural models, only a few attempts have been made to extend a monaural model by a binaural stage using a unified decision stage for monaural and binaural cues. A typical prototype of binaural processing has been the classical equalization-cancelation mechanism, which either involves signal-adaptive delays and provides a single channel output, or can be implemented with tapped delays providing a high-dimensional multichannel output. This contribution extends the (monaural) generalized envelope power spectrum model by a non-adaptive binaural stage with only a few, fixed output channels. The binaural stage resembles features of physiologically motivated hemispheric binaural processing, as simplified signal-processing stages, yielding a 5-channel monaural and binaural matrix feature “decoder” (BMFD). The back end of the existing monaural model is applied to the BMFD output and calculates short-time envelope power and power features. The resulting model accounts for several published psychoacoustic and speech-intelligibility experiments and achieves a prediction performance comparable to existing state-of-the-art models with more complex binaural processing.

Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch.

Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch. [ABSTRACT FROM AUTHOR]

Both harmonic and binaural signal properties are relevant for auditory processing. To investigate how these cues combine in the auditory system, detection thresholds for an 800-Hz tone masked by a diotic (i.e., identical between the ears) harmonic complex tone were measured in six normal-hearing subjects. The target tone was presented either diotically or with an interaural phase difference (IPD) of 180° and in either harmonic or “mistuned” relationship to the diotic masker. Three different maskers were used, a resolved and an unresolved complex tone (fundamental frequency: 160 and 40 Hz) with four components below and above the target frequency and a broadband unresolved complex tone with 12 additional components. The target IPD provided release from masking in most masker conditions, whereas mistuning led to a significant release from masking only in the diotic conditions with the resolved and the narrowband unresolved maskers. A significant effect of mistuning was neither found in the diotic condition with the wideband unresolved masker nor in any of the dichotic conditions. An auditory model with a single analysis frequency band and different binaural processing schemes was employed to predict the data of the unresolved masker conditions. Sensitivity to modulation cues was achieved by including an auditory-motivated modulation filter in the processing pathway. The predictions of the diotic data were in line with the experimental results and literature data in the narrowband condition, but not in the broadband condition, suggesting that across-frequency processing is involved in processing modulation information. The experimental and model results in the dichotic conditions show that the binaural processor cannot exploit modulation information in binaurally unmasked conditions. [ABSTRACT FROM AUTHOR]

This letter discusses the application of models of temporal patterns of auditory-nerve firings to enhance robustness of automatic speech recognition systems. Most conventional feature extraction schemes (such as mel-frequency cepstral coefficients and perceptual linear processing coefficients) are based on short-time energy in each frequency band, and the temporal patterns of auditory-nerve activity are discarded. We compare the impact on speech recognition accuracy of several types of feature extraction schemes based on the putative synchrony of auditory-nerve activity, including feature extraction based on a modified version of the generalized synchrony detector proposed by Seneff, and a modified version of the averaged localized synchrony response proposed by Young and Sachs. It was found that the use of features based on auditory-nerve synchrony can indeed improve speech recognition accuracy in the presence of additive noise based on experiments using multiple standard speech databases. Recognition accuracy obtained using the synchrony-based features is further increased if some form of noise removal is applied to the signal before the synchrony measure is estimated. Signal processing for noise removal based on the noise suppression that is a part of PNCC feature extraction is more effective toward this end than conventional spectral subtraction. [ABSTRACT FROM AUTHOR]

Sound-source localization in the horizontal plane relies on detecting small differences in the timing and level of the sound at the two ears, including differences in the timing of the modulated envelopes of high-frequency sounds (envelope interaural time differences (ITDs)). We investigated responses of single neurons in the inferior colliculus (IC) to a wide range of envelope ITDs and stimulus envelope shapes. By a novel means of visualizing neural activity relative to different portions of the periodic stimulus envelope at each ear, we demonstrate the role of neuron-specific excitatory and inhibitory inputs in creating ITD sensitivity (or the lack of it) depending on the specific shape of the stimulus envelope. The underlying binaural brain circuitry and synaptic parameters were modeled individually for each neuron to account for neuron-specific activity patterns. The model explains the effects of envelope shapes on sensitivity to envelope ITDs observed in both normal-hearing listeners and in neural data, and has consequences for understanding how ITD information in stimulus envelopes might be maximized in users of bilateral cochlear implants-for whom ITDs conveyed in the stimulus envelope are the only ITD cues available. [ABSTRACT FROM AUTHOR]

A computationally efficient model for pitch estimation of mixed audio signals is presented. Pitch estimation plays a significant role in music audition like music information retrieval, automatic music transcription, melody extraction etc. The proposed system consists of channel separation and periodicity detection. The input signal is created by mixing two sound signals. First removes the short time correlations of the mixed signal. The model divides the signal into number of channels using wavelet packet transform. Computes the cepstrum of each channel and sums the cepstrum functions. The summary cepstrum function is further processed to extract the pitch frequency of two input signal separately. The model performance is demonstrated to be comparable to those of recent multichannel models. The proposed system can be verified by simulating the system in MATLAB. [ABSTRACT FROM AUTHOR]

We present an example of an anthropomorphic approach, in which auditory-based cues are combined with temporal correlation to implement a source separation system. The auditory features are based on spectral amplitude modulation and energy information obtained through 256 cochlear filters. Segmentation and binding of auditory objects are performed with a two-layered spiking neural network. The first layer performs the segmentation of the auditory images into objects, while the second layer binds the auditory objects belonging to the same source. The binding is further used to generate a mask (binary gain) to suppress the undesired sources from the original signal. Results are presented for a double-voiced (2 speakers) speech segment and for sentences corrupted with different noise sources. Comparative results are also given using PESQ (perceptual evaluation of speech quality) scores. The spiking neural network is fully adaptive and unsupervised.

• Unique and comprehensive SRT data set with 40 listeners with various hearing loss. • Compare machine-learning-based FADE with Plomps empirical A & D model. • Precise prediction of SRT (RMS error of 3.3 dB), A and D component. • FADE is advantageous for consistent & precise modeling & individualization. • For clinical purposes, a linear regression for the D component appears sufficient. Plomp introduced an empirical separation of the increased speech recognition thresholds (SRT) in listeners with a sensorineural hearing loss into an Attenuation (A) component (which can be compensated by amplification) and a non-compensable Distortion (D) component. Previous own research backed up this notion by speech recognition models that derive their SRT prediction from the individual audiogram with or without a psychoacoustic measure of suprathreshold processing deficits. To determine the precision in separating the A and D component for the individual listener with various individual measures and individualized models, SRTs with 40 listeners with a variation in hearing impairment were obtained in quiet, stationary noise, and fluctuating noise (ICRA 5–250 and babble). Both the clinical audiogram and an adaptive, precise sweep audiogram were obtained as well as tone-in-noise detection thresholds at four frequencies to characterize the individual hearing impairment. For predicting the SRT, the FADE-model (which is based on machine learning) was used with either of the two audiogram procedures and optionally the individual tone-in-noise detection thresholds. The results indicate that the precisely measured swept tone audiogram allows for a more precise prediction of the individual SRT in comparison to the clinical audiogram (RMS error of 4.3 dB vs. 6.4 dB, respectively). While an estimation from the precise audiogram and FADE performed equally well in predicting the individual A and D component, the further refinement of including the tone-in-noise detection threshold with FADE led to a slight improvement of prediction accuracy (RMS error of 3.3 dB, 4.6 dB and 1.4 dB, for SRT, A and D component, respectively). Hence, applying FADE is advantageous for scientific purposes where a consistent modeling of different psychoacoustical effects in the same listener with a minimum amount of assumptions is desirable. For clinical purposes, however, a precisely measured audiogram and an estimation of the expected D component using a linear regression appears to be a satisfactory first step towards precision audiology. [ABSTRACT FROM AUTHOR]

• Speech intelligibility measured with maskers ranging from noise to speech. • Behavioral data compared to predictions from speech intelligibility models. • No strong evidence for harmonicity-based effects with speech maskers. • Harmonic cancellation not a crucial component of speech intelligibility models. This study investigated the role of harmonic cancellation in the intelligibility of speech in "cocktail party" situations. While there is evidence that harmonic cancellation plays a role in the segregation of simple harmonic sounds based on fundamental frequency (F0), its utility for mixtures of speech containing non-stationary F0s and unvoiced segments is unclear. Here we focused on the energetic masking of speech targets caused by competing speech maskers. Speech reception thresholds were measured using seven maskers: speech-shaped noise, monotonized and intonated harmonic complexes, monotonized speech, noise-vocoded speech, reversed speech and natural speech. These maskers enabled an estimate of how the masking potential of speech is influenced by harmonic structure, amplitude modulation and variations in F0 over time. Measured speech reception thresholds were compared to the predictions of two computational models, with and without a harmonic cancellation component. Overall, the results suggest a minor role of harmonic cancellation in reducing energetic masking in speech mixtures. [ABSTRACT FROM AUTHOR]

• Across-frequency fluctuation profiles predict speech intelligibility in noise • Model predicts effects of level and different noise types, incl. masking release • Model predicts effects of individual hearing loss • Plausible effects of inner- and outer-hair-cell impairment This study presents a major update and full evaluation of a speech intelligibility (SI) prediction model previously introduced by Scheidiger, Carney, Dau, and Zaar [(2018), Acta Acust. United Ac. 104 , 914-917]. The model predicts SI in speech-in-noise conditions via comparison of the noisy speech and the noise-alone reference. The two signals are processed through a physiologically inspired nonlinear model of the auditory periphery, for a range of characteristic frequencies (CFs), followed by a modulation analysis in the range of the fundamental frequency of speech. The decision metric of the model is the mean of a series of short-term, across-CF correlations between population responses to noisy speech and noise alone, with a sensitivity-limitation process imposed. The decision metric is assumed to be inversely related to SI and is converted to a percent-correct score using a single data-based fitting function. The model performance was evaluated in conditions of stationary, fluctuating, and speech-like interferers using sentence-based speech-reception thresholds (SRTs) previously obtained in 5 normal-hearing (NH) and 13 hearing-impaired (HI) listeners. For the NH listener group, the model accurately predicted SRTs across the different acoustic conditions (apart from a slight overestimation of the masking release observed for fluctuating maskers), as well as plausible effects in response to changes in presentation level. For HI listeners, the model was adjusted to account for the individual audiograms using standard assumptions concerning the amount of HI attributed to inner-hair-cell (IHC) and outer-hair-cell (OHC) impairment. HI model results accounted remarkably well for elevated individual SRTs and reduced masking release. Furthermore, plausible predictions of worsened SI were obtained when the relative contribution of IHC impairment to HI was increased. Overall, the present model provides a useful tool to accurately predict speech-in-noise outcomes in NH and HI listeners, and may yield important insights into auditory processes that are crucial for speech understanding. [ABSTRACT FROM AUTHOR]

• A binaural non-stationary harmonic-cancellation speech intelligibility model. • Predictions of SRTs for speech masked by a harmonic complex. • Accounts for spatial separation, intonation and amplitude modulation of the masker. • Harmonic cancellation and binaural unmasking might be mutually exclusive mechanisms. The aim of this study was to extend the harmonic-cancellation model proposed by Prud'homme et al. [J. Acoust. Soc. Am. 148 (2020) 3246-–3254] to predict speech intelligibility against a harmonic masker, so that it takes into account binaural hearing, amplitude modulations in the masker and variations in masker fundamental frequency (F0) over time. This was done by segmenting the masker signal into time frames and combining the previous long-term harmonic-cancellation model with the binaural model proposed by Vicente and Lavandier [Hear. Res. 390 (2020) 107937]. The new model was tested on the data from two experiments involving harmonic complex maskers that varied in spatial location, temporal envelope and F0 contour. The interactions between the associated effects were accounted for in the model by varying the time frame duration and excluding the binaural unmasking computation when harmonic cancellation is active. Across both experiments, the correlation between data and model predictions was over 0.96, and the mean and largest absolute prediction errors were lower than 0.6 and 1.5 dB, respectively. [ABSTRACT FROM AUTHOR]

Noise robustness has long been one of the most important goals in speech recognition. While the performance of automatic speech recognition (ASR) deteriorates in noisy situations, the human auditory system is relatively adept at handling noise. To mimic this adeptness, we study and apply psychoacoustic models in speech recognition as a means to improve robustness of ASR systems. Psychoacoustic models are usually implemented in a subtractive manner with the intention to remove noise. However, this is not necessarily the only approach to this challenge. This paper presents a novel algorithm which implements psychoacoustic models additively. The algorithm is motivated by the fact that weak sound elements that are below the masking threshold are the same for the human auditory system, regardless of the actual sound pressure level. Another important contribution of our proposed algorithm is a superior implementation of masking effect. Only those sounds that fall below the masking threshold are modified, which better reflects physical masking effects. We give detailed experimental results showing relationships between the subtractive and additive approaches. Since all the parameters of the proposed filters are positive or zero, they are named 2D psychoacoustic P-filters. Detailed theoretical analysis is provided to show the noise removal ability of these filters. Experiments are carried out on the AURORA2 database. Experimental results show that the word recognition rate using our proposed feature extraction method has been effectively increased. Given models trained with clean speech, our proposed method achieves up to 84.23% word recognition on noisy data. [ABSTRACT FROM AUTHOR]

When a high harmonic is removed from a cosine-phase harmonic complex, we hear a sine tone pop out of the perception; the sine tone has the pitch of the high harmonic, while the tone complex has the pitch of its fundamental frequency, f0. This phenomenon is commonly referred to as Duifhuis Pitch (DP). This paper describes, for the first time, the cortical representation of DP observed with magnetoencephalography. In experiment 1, conditions that produce the perception of a DP were observed to elicit a classic onset response in auditory cortex (P1m, N1m, P2m), and an increment in the sustained field (SF) established in response to the tone complex. Experiment 2 examined the effect of the phase spectrum of the complex tone on the DP activity: Schroederphase negative waves elicited a transient DP complex with a similar shape to that observed with cosine-phase waves but with much longer latencies. Following the transient DP activity, the responses of the negative and positive Schroeder-phase waves converged, and the increment in the SF slowly died away. In the absence of DP, the two Schroeder-phase conditions with low peak factors both produced larger SFs than cosine-phase waves with large peak factors. A model of the auditory periphery that includes coupling between adjacent frequency channels is used to explain the early neuromagnetic activity observed in auditory cortex. [ABSTRACT FROM AUTHOR]

Abstract: Performance of an automatic speech recognition system drops dramatically in the presence of background noise unlike the human auditory system which is more adept at noisy speech recognition. This paper proposes a novel auditory modeling algorithm which is integrated into the feature extraction front-end for Hidden Markov Model (HMM). The proposed algorithm is named LTFC which simulates properties of the human auditory system and applies it to the speech recognition system to enhance its robustness. It integrates simultaneous masking, temporal masking and cepstral mean and variance normalization into ordinary mel-frequency cepstral coefficients (MFCC) feature extraction algorithm for robust speech recognition. The proposed method sharpens the power spectrum of the signal in both the frequency domain and the time domain. Evaluation tests are carried out on the AURORA2 database. Experimental results show that the word recognition rate using our proposed feature extraction method has been effectively increased. [Copyright &y& Elsevier]

Abstract: Humans show a very robust ability to localize sounds in adverse conditions. Computational models of binaural sound localization and technical approaches of direction-of-arrival (DOA) estimation also show good performance, however, both their binaural feature extraction and the strategies for further analysis partly differ from what is currently known about the human auditory system. This study investigates auditory model based DOA estimation emphasizing known features and limitations of the auditory binaural processing such as (i) high temporal resolution, (ii) restricted frequency range to exploit temporal fine-structure, (iii) use of temporal envelope disparities, and (iv) a limited range to compensate for interaural time delay. DOA estimation performance was investigated for up to five concurrent speakers in free field and for up to three speakers in the presence of noise. The DOA errors in these conditions were always smaller than 5°. A condition with moving speakers was also tested and up to three moving speakers could be tracked simultaneously. Analysis of DOA performance as a function of the binaural temporal resolution showed that short time constants of about 5ms employed by the auditory model were crucial for robustness against concurrent sources. [Copyright &y& Elsevier]

Abstract: The effects of hearing impairment on the perception of binaural-pitch stimuli were investigated. Several experiments were performed with normal-hearing and hearing-impaired listeners, including detection and discrimination of binaural pitch, and melody recognition using different types of binaural pitches. For the normal-hearing listeners, all types of binaural pitches could be perceived immediately and were musical. The hearing-impaired listeners could be divided into three groups based on their results: (a) some perceived all types of binaural pitches, but with decreased salience or musicality compared to normal-hearing listeners; (b) some could only perceive the strongest pitch types; (c) some were unable to perceive any binaural pitch at all. The performance of the listeners was not correlated with audibility. Additional experiments investigated the correlation between performance in binaural-pitch perception and performance in measures of spectral and temporal resolution. Reduced frequency discrimination appeared to be linked to poorer melody recognition skills. Reduced frequency selectivity was also found to impede the perception of binaural-pitch stimuli. Overall, binaural-pitch stimuli might be very useful tools within clinical diagnostics for detecting specific deficiencies in the auditory system. [Copyright &y& Elsevier]

Abstract: Steady-state responses were evoked by noise stimuli that alternated between two levels of interaural correlation ρ at a frequency f m. With ρ alternating between +1 and 0, responses at f m dropped steeply above 4Hz, but persisted up to 64Hz. Two time constants of 47 and 4.4ms with delays of 198 and 36ms, respectively, were obtained by fitting responses to a transfer function based on symmetric exponential windows. The longer time constant, possibly reflecting cortical integration, is consistent with perceptual binaural “sluggishness”. The shorter time constant may reflect running cross-correlation in the high brainstem or primary auditory cortex. Responses at 2f m peaked with an amplitude of 848±479nV (f m =4Hz). Investigation of this robust response revealed that: (1) changes in ρ and lateralization evoked similar responses, suggesting a common neural origin, (2) response was most dependent on stimulus frequencies below 1000Hz, but frequencies up to 4000Hz also contributed, and (3) when ρ alternated between [0.2–1] and 0, response amplitude varied linearly with ρ, and the physiological response threshold was close to the average behavioral threshold (ρ =0.31). This steady-state response may prove useful in the objective investigation of binaural hearing. [Copyright &y& Elsevier]

• Modifying the stimulus envelope to evoke more synchronous auditory-nerve firing, enhances the EFR. • Improved EFR analysis methods include multiple harmonics and correct for the individual noise floor. • EFRs to stimuli with rectangular envelopes (duty cycle 20–25%) are optimal for synaptopathy diagnosis. • Older NH and HI listeners had reduced RAM-EFRs, suggesting that they had age-related synaptopathy. • Sensitive diagnostic markers of synaptopathy are crucial when studying its perceptual consequences. Auditory de-afferentation, a permanent reduction in the number of inner-hair-cells and auditory-nerve synapses due to cochlear damage or synaptopathy, can reliably be quantified using temporal bone histology and immunostaining. However, there is an urgent need for non-invasive markers of synaptopathy to study its perceptual consequences in live humans and to develop effective therapeutic interventions. While animal studies have identified candidate auditory-evoked-potential (AEP) markers for synaptopathy, their interpretation in humans has suffered from translational issues related to neural generator differences, unknown hearing-damage histopathologies or lack of measurement sensitivity. To render AEP-based markers of synaptopathy more sensitive and differential to the synaptopathy aspect of sensorineural hearing loss, we followed a combined computational and experimental approach. Starting from the known characteristics of auditory-nerve physiology, we optimized the stimulus envelope to stimulate the available auditory-nerve population optimally and synchronously to generate strong envelope-following-responses (EFRs). We further used model simulations to explore which stimuli evoked a response that was sensitive to synaptopathy, while being maximally insensitive to possible co-existing outer-hair-cell pathologies. We compared the model-predicted trends to AEPs recorded in younger and older listeners (N=44, 24f) who had normal or impaired audiograms with suspected age-related synaptopathy in the older cohort. We conclude that optimal stimulation paradigms for EFR-based quantification of synaptopathy should have sharply rising envelope shapes, a minimal plateau duration of 1.7-2.1 ms for a 120-Hz modulation rate, and inter-peak intervals which contain near-zero amplitudes. From our recordings, the optimal EFR-evoking stimulus had a rectangular envelope shape with a 25% duty cycle and a 95% modulation depth. Older listeners with normal or impaired audiometric thresholds showed significantly reduced EFRs, which were consistent with how (age-induced) synaptopathy affected these responses in the model. [ABSTRACT FROM AUTHOR]

Collin and Lavandier [J. Acoust. Soc. Am. 134, 1146–1159 (2013)] proposed a binaural model predicting speech intelligibility against envelope-modulated noises, evaluated in 24 acoustic conditions, involving similar masker types. The aim of the present study was to test the model robustness modeling 80 additional conditions, and evaluate the influence of its parameters using an approach inspired by a variance-based sensitivity analysis. First, the data from four experiments from the literature and one specifically designed for the present study were used to evaluate the prediction performance of the model, investigate potential interactions between its parameters, and define their values leading to the best predictions. A revision of the model allowed to account for binaural sluggishness. Finally, the optimized model was tested on an additional dataset not used to define its parameters. Overall, one hundred conditions split into six experiments were modeled. Correlation between data and predictions ranged from 0.85 to 0.96 across experiments, and mean absolute prediction errors were between 0.5 and 1.4 dB. • A binaural model predicting speech intelligibility in modulated noises is tested. • Speech reception thresholds measured in 100 conditions are accurately predicted. • The effect of binaural sluggishness on binaural unmasking is implemented. • Better-ear listening probably involves monaural and binaural temporal resolutions. [ABSTRACT FROM AUTHOR]