Electroacoustic and Behavioural Evaluation of Hearing Aid Digital Signal Processing Features

Modern digital hearing aids provide an array of features to improve the user listening experience. As the features become more advanced and interdependent, it becomes increasingly necessary to develop accurate and cost-effective methods to evaluate their performance. Subjective experiments are an accurate method to determine hearing aid performance but they come with a high monetary and time cost. Four studies that develop and evaluate electroacoustic hearing aid feature evaluation techniques are presented. The first study applies a recent speech quality metric to two bilateral wireless hearing aids with various features enabled in a variety of environmental conditions. The study shows that accurate speech quality predictions are made with a reduced version of the original metric, and that a portion of the original metric does not perform well when applied to a novel subjective speech quality rating database. The second study presents a reference free (non-intrusive) electroacoustic speech quality metric developed specifically for hearing aid applications and compares its performance to a recent intrusive metric. The non-intrusive metric offers the advantage of eliminating the need for a shaped reference signal and can be used in real time applications but requires a sacrifice in prediction accuracy. The third study investigates the digital noise reduction performance of seven recent hearing aid models. An electroacoustic measurement system is presented that allows the noise and speech signals to be separated from hearing aid recordings. It is shown how this can be used to investigate digital noise reduction performance through the application of speech quality and speech intelligibility measures. It is also shown how the system can be used to quantify digital noise reduction attack times. The fourth study presents a turntable-based system to investigate hearing aid directionality performance. Two methods to extract the signal of interest are described. Polar plots are presented for a number of hearing aid models from recordings generated in both the free-field and from a head-and-torso simulator. It is expected that the proposed electroacoustic techniques will assist Audiologists and hearing researchers in choosing, benchmarking, and fine-tuning hearing aid features

Effects of Coordinated Bilateral Hearing Aids and Auditory Training on Sound Localization

This thesis has two main objectives: 1) evaluating the benefits of the bilateral coordination of the hearing aid Digital Signal Processing (DSP) features by measuring and comparing the auditory performance with and without the activation of this coordination, and 2) evaluating the benefits of acclimatization and auditory training on such auditory performance and, determining whether receiving training in one aspect of auditory performance (sound localization) would generalize to an improvement in another aspect of auditory performance (speech intelligibility in noise), and to what extent. Two studies were performed. The first study evaluated the speech intelligibility in noise and horizontal sound localization abilities in HI listeners using hearing aids that apply bilateral coordination of WDRC. A significant improvement was noted in sound localization with bilateral coordination on when compared to off, while speech intelligibility in noise did not seem to be affected. The second study was an extension of the first study, with a suitable period for acclimatization provided and then the participants were divided into training and control groups. Only the training group received auditory training. The training group performance was significantly better than the control group performance in some conditions, in both the speech intelligibility and the localization tasks. The bilateral coordination did not have significant effects on the results of the second study. This work is among the early literature to investigate the impact of bilateral coordination in hearing aids on the users’ auditory performance. Also, this work is the first to demonstrate the effect of auditory training in sound localization on the speech intelligibility performance

Comparison of Binaural RTF-Vector-Based Direction of Arrival Estimation Methods Exploiting an External Microphone

In this paper we consider a binaural hearing aid setup, where in addition to the head-mounted microphones an external microphone is available. For this setup, we investigate the performance of several relative transfer function (RTF) vector estimation methods to estimate the direction of arrival(DOA) of the target speaker in a noisy and reverberant acoustic environment. More in particular, we consider the state-of-the-art covariance whitening (CW) and covariance subtraction (CS) methods, either incorporating the external microphone or not, and the recently proposed spatial coherence (SC) method, requiring the external microphone. To estimate the DOA from the estimated RTF vector, we propose to minimize the frequency-averaged Hermitian angle between the estimated head-mounted RTF vector and a database of prototype head-mounted RTF vectors. Experimental results with stationary and moving speech sources in a reverberant environment with diffuse-like noise show that the SC method outperforms the CS method and yields a similar DOA estimation accuracy as the CW method at a lower computational complexity.Comment: Submitted to EUSIPCO 202

Influence of Lossy Speech Codecs on Hearing-aid, Binaural Sound Source Localisation using DNNs

Hearing aids are typically equipped with multiple microphones to exploit spatial information for source localisation and speech enhancement. Especially for hearing aids, a good source localisation is important: it not only guides source separation methods but can also be used to enhance spatial cues, increasing user-awareness of important events in their surroundings. We use a state-of-the-art deep neural network (DNN) to perform binaural direction-of-arrival (DoA) estimation, where the DNN uses information from all microphones at both ears. However, hearing aids have limited bandwidth to exchange this data. Bluetooth low-energy (BLE) is emerging as an attractive option to facilitate such data exchange, with the LC3plus codec offering several bitrate and latency trade-off possibilities. In this paper, we investigate the effect of such lossy codecs on localisation accuracy. Specifically, we consider two conditions: processing at one ear vs processing at a central point, which influences the number of channels that need to be encoded. Performance is benchmarked against a baseline that allows full audio-exchange - yielding valuable insights into the usage of DNNs under lossy encoding. We also extend the Pyroomacoustics library to include hearing-device and head-related transfer functions (HD-HRTFs) to suitably train the networks. This can also benefit other researchers in the field

Contributions to speech processing and ambient sound analysis

We are constantly surrounded by sounds that we continuously exploit to adapt our actions to situations we are facing. Some of the sounds like speech can have a particular structure from which we can infer some information, explicit or not. This is one reason why speech is possibly that is the most intuitive way to communicate between humans. Within the last decade, there has been significant progress in the domain of speech andaudio processing and in particular in the domain of machine learning applied to speech and audio processing. Thanks to these progresses, speech has become a central element in many human to human distant communication tools as well as in human to machine communication systems. These solutions work pretty well on clean speech or under controlled condition. However, in scenarios that involve the presence of acoustic perturbation such as noise or reverberation systems performance tends to degrade severely. In this thesis we focus on processing speech and its environments from an audio perspective. The algorithms proposed here are relying on a variety of solutions from signal processing based approaches to data-driven solutions based on supervised matrix factorization or deep neural networks. We propose solutions to problems ranging from speech recognition, to speech enhancement or ambient sound analysis. The target is to offer a panorama of the different aspects that could improve a speech processing algorithm working in a real environments. We start by describing automatic speech recognition as a potential end application and progressively unravel the limitations and the proposed solutions ending-up to the more general ambient sound analysis.Nous sommes constamment entourés de sons que nous exploitons pour adapter nos actions aux situations auxquelles nous sommes confrontés. Certains sons comme la parole peuvent avoir une structure particulière à partir de laquelle nous pouvons déduire des informations, explicites ou non. C’est l’une des raisons pour lesquelles la parole est peut-être le moyen le plus intuitif de communiquer entre humains. Au cours de la décennie écoulée, des progrès significatifs ont été réalisés dans le domaine du traitement de la parole et du son et en particulier dans le domaine de l’apprentissage automatique appliqué au traitement de la parole et du son. Grâce à ces progrès, la parole est devenue un élément central de nombreux outils de communication à distance d’humain à humain ainsi que dans les systèmes de communication humain-machine. Ces solutions fonctionnent bien sur un signal de parole propre ou dans des conditions contrôlées. Cependant, dans les scénarios qui impliquent la présence de perturbations acoustiques telles que du bruit ou de la réverbération les performances peuvent avoir tendance à se dégrader gravement. Dans cette HDR, nous nous concentrons sur le traitement de la parole et de son environnement d’un point de vue audio. Les algorithmes proposés ici reposent sur une variété de solutions allant des approches basées sur le traitement du signal aux solutions orientées données à base de factorisation matricielle supervisée ou de réseaux de neurones profonds. Nous proposons des solutions à des problèmes allant de la reconnaissance vocale au rehaussement de la parole ou à l’analyse des sons ambiants. L’objectif est d’offrir un panorama des différents aspects qui pourraient être améliorer un algorithme de traitement de la parole fonctionnant dans un environnement réel. Nous commençons par décrire la reconnaissance automatique de la parole comme une application finale potentielle et analysons progressivement les limites et les solutions proposées aboutissant à l’analyse plus générale des sons ambiants