Assistive listening systems (ALSs) for people with hearing difficulties have traditionally utilized magnetic induction hearing loops compatible with hearing aids (HAs) or headset systems connected via RF or IR links. The HA industry has been working with the Bluetooth SIG over the past decade to develop a new low-energy audio standard with broadcast capabilities. Released in 2022 as Bluetooth LE Audio with Auracast, it is poised to replace legacy ALSs, with potential compatibility across all future HAs and smart earbuds (a.k.a. "hearables").However, several technical and practical challenges remain for the replacement of traditional ALSs by Auracast. Firstly, the recommended calibration of sound volume levels in Auracast transmitters and receivers mirrors the standard for calibrating hearing loops, but hearing loops are normally installed by trained professionals, whereas the relative simplicity of Auracast transmitters means that there is a likelihood that they will be set up by non-experts in many cases. Similarly, in large venues it is possible for installers of hearing loops to insert a time delay in the loop signal processing to match the time delay of the acoustic signal arriving through the air at the position of the loop in the venue. However, since Auracast users will not necessarily be constrained to one position in the venue, they will experience a range of different acoustic time delays.In this paper, we: i) review the development of the Bluetooth LE Audio with Auracast standard and how it compares to traditional ALS technologies, and ii) discuss signal processing approaches to sound level calibration and variable audio delay implementation in Auracast transmitters and receivers.

Computational models of the auditory periphery are important tools for understanding mechanisms of normal and impaired hearing and for developing advanced speech and audio processing algorithms. However, the simulation of accurate neural representations entails a high computational effort. This prevents the use of auditory models in applications with real-time requirements and the design of speech enhancement algorithms based on efficient bio-inspired optimization criteria. Hence, in this work we propose and evaluate DNN-based approximations of a state-of-the-art auditory model. The DNN models yield accurate neurogram predictions for previously unseen speech signals with processing times shorter than signal duration, thus indicating their potential to be applied in real-time.

This paper investigates the use of voicing information as an additional cue in contrast enhanced frequency shaping (CEFS) of speech to improve perception in the hearing impaired. The presented work builds on an existing system combining multiband compression with contrast enhanced frequency shaping (MICEFS) to restore the auditory nerve response of a hearing impaired listener. CEFS can improve the perception of voiced segments. Hence voicing cues are used to differentiate segments for processing. Alternative processing for unvoiced segments is investigated and shown to improve neural representation of unvoiced segments compared to using MICEFS processing alone.

Hearing loss due to peripheral damage is associated with cochlear hair cell damage or loss and some retrograde degeneration of auditory nerve fibers. Surviving auditory nerve fibers in the impaired region exhibit elevated and broadened frequency tuning, and the cochleotopic representation of broadband stimuli such as speech is distorted. In impaired cortical regions, increased tuning to frequencies near the edge of the hearing loss coupled with increased spontaneous and synchronous firing is observed. Tinnitus, an auditory percept in the absence of sensory input, may arise under these circumstances as a result of plastic reorganization in the auditory cortex. We present a spiking neuron model of auditory cortex that captures several key features of cortical organization. A key assumption in the model is that in response to reduced afferent excitatory input in the damaged region, a compensatory change in the connection strengths of lateral excitatory and inhibitory connections occurs. These changes allow the model to capture some of the cortical correlates of sensorineural hearing loss, including changes in spontaneous firing and synchrony; these phenomena may explain central tinnitus. This model may also be useful for evaluating procedures designed to segregate synchronous activity underlying tinnitus and for evaluating adaptive hearing devices that compensate for selective hearing loss.

Several algorithms have been developed for tracking formant frequency trajectories of speech signals, however most of these algorithms are either not robust in real-life noise environments or are not suitable for real-time implementation. The algorithm presented in this paper obtains formant frequency estimates from voiced segments of continuous speech by using a time-varying adaptive filterbank to track individual formant frequencies. The formant tracker incorporates an adaptive voicing detector and a gender detector for formant extraction from continuous speech, for both male and female speakers. The algorithm has a low signal delay and provides smooth and accurate estimates for the first four formant frequencies at moderate and high signal-to-noise ratios. Thorough testing of the algorithm has shown that it is robust over a wide range of signal-to-noise ratios for various types of background noises.

We propose a novel model-based hearing compensation strategy and gradient-free optimization procedure for a learning-based hearing aid design. Motivated by physiological data and normal and impaired auditory nerve models, a hearing compensation strategy is cast as a neural coding problem, and a Neurocompensator is designed to compensate for the hearing loss and enhance the speech. With the goal of learning the Neurocompensator parameters, we use a gradient-free optimization procedure, an improved version of the ALOPEX that we have developed, to learn the unknown parameters of the Neurocompensator. We present our methodology, learning procedure, and experimental results in detail; discussion is also given regarding the unsupervised learning and optimization methods.

Simon Haykin Dept. of Electrical Engineering McMaster University [email protected] A major issue in evaluating speech enhancement and hearing compensation algorithms is to come up with a suitable metric that predicts intelligibility as judged by a human listener. Previous methods such as the widely used Speech Transmission Index (STI) fail to account for masking effects that arise from the highly nonlinear cochlear transfer function. We therefore propose a Neural Articulation speech intelligibility from the instantaneous neural spike rate over time, produced when a signal is processed by an auditory neural model. By using a well developed model of the auditory periphery and detection theory we show that human perceptual discrimination closely matches the modeled distortion in the instantaneous spike rates of the auditory nerve. In highly rippled frequency transfer conditions the NAI’s prediction error is 8% versus the STI’s prediction error of 10.8%.

While many algorithms exist for accurate extraction of formant frequencies from a speech waveform, these algorithms are not typically shown to be robust in the presence of highly-transient background noise such as competing speech waveforms. Preliminary results are presented from an algorithm using time-varying adaptive filters that appears to be robust in the presence of white, Gaussian noise or a single competing speaker over a large range of signal-to-noise ratios (quiet to −6 dB). Use of a synthesized sentence, for which the actual formant frequencies are known, permits quantitative assessment of the algorithm's accuracy as a function of signal-to-noise ratio.

Many theories of hearing hypothesize that spatiotemporal combination of single-fiber activity forms an important feature of the auditory system. One mechanism which has been proposed to explain perceptual performance is spatiotemporal summation of Auditory Nerve (AN) firings. Anatomical and physiological studies have found a number of different cell types in the Cochlear Nucleus (CN) which appear to perform such summations. The purpose of this paper is to determine theoretically how various degrees of AN fiber convergence onto summing neurons in the CN would affect estimation and discrimination of stimulus features from the summing neurons' outputs. To achieve this, we utilize an analytical model, based on a point process representation of AN activity, which includes parameterized measures of stimulus properties. The summing neuron model allows for an arbitrary degree of AN fiber convergence, thus facilitating an investigation of the feasibility of theories of neural coding of sound for different summing neuron types. Results obtained from the model indicate that for coding of the stimulus frequency in the periodicity of firings, the optimal spread of input fibers is very narrow and temporal integration is very short. This suggests that AN fibers which converge onto CN cells exhibiting enhanced synchronization originate from sites on the basilar membrane very close together. In contrast, CN cell types which exhibit reduced synchronization could be useful in performing estimation and discrimination tasks based on the average discharge rate. The model indicates that this could be achieved by a very wide spread of inputs and/or long-term temporal integration. We postulate that a wide spread of inputs would be preferential to long-term temporal integration, because this would enable the neuron's output to follow rapid changes in the average discharge rate of a population of fibers. Consequently, transients in the stimulus intensity which produce rapid changes in the average discharge rate of a population of fibers could be detected by a rapid change in the output of such a summing neuron.