Algoritmo adaptativo para redução de ruído e preservação de pistas acústicas biauriculares para aparelhos auditivos

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia Eletrônica.Aparelhos auditivos são tecnologias assistivas que têm como objetivo a compensação de limitações auditivas. Os modelos biauriculares utilizam comunicação entre os dispositivos em ambas as orelhas, permitindo maior desempenho tanto na redução de ruído quanto na manutenção das características espaciais do cenário acústico. O filtro de Wiener biauricular multicanal (BMWF) é um estimador linear que reduz o sinal interferente (ruído) e preserva as pistas acústicas biauriculares da fala, entretanto distorce as pistas associadas ao ruído, o que resulta em deslocamento aparente da localização do sinal interferente. Assim, o presente trabalho apresenta uma proposta de método para redução de ruído e preservação de pistas acústicas biauriculares do ruído. O método desenvolvido baseia-se no BMWF e utiliza uma função custo composta pela medida de diferença de nível interauricular (ILD) e de coerência interauricular (IC), sendo denominado MWF-ILD-IC. É possível demonstrar que a preservação da coerência interauricular preserva a diferença de tempo interauricular (ITD), também interpretável como uma diferença de fase interauricular (IPD). Resultados de métricas objetivas do algoritmo adaptativo proposto sugerem que o filtro é capaz de preservar as pistas biauriculares do ruído, tanto em cenários acústicos com fonte sonora pontual quanto em cenários sujeitos a campos sonoros difusos, tendo, em contrapartida, menor capacidade de redução de ruído e, por conseguinte, menor ganho na qualidade da fala em relação ao BMWF convencional. O comportamento antagônico entre a razão sinal-ruído e erro associado às pistas biauriculares indica que é necessário avaliar com cautela o compromisso entre as variáveis, de forma que projetistas configurem parâmetros apropriados para cada situação de uso.Hearing aids are assistive technologies that aim to compensate hearing impairments. The binaural approach employs communication between devices in both ears, allowing greater performance both in noise reduction and in the preservation of the original acoustic scenario perception. The Binaural Multichannel Wiener Filter (BMWF) is a linear estimator that reduces the interfering signal (noise) and preserves the binaural acoustic cues of speech, however it distorts the cues associated with noise, which results in an apparent displacement of the location of the interfering signal. Thus, this work presents a new method for noise reduction and preservation of noise binaural cues. The method developed is based on the BMWF and uses an auxiliary cost function based on input-output difference of the Interaural Level Difference (ILD) and the Interaural Coherence (IC), called MWFILD-IC. It is possible to demonstrate that the preservation of interaural coherence also preserves the Interaural Time Difference (ITD), also interpretable as an Interaural Phase Difference (IPD). Objective metrics results of the proposed adaptive algorithm suggest that the filter is able to preserve the noise binaural cues, both in acoustic scenarios with punctual sound source and diffuse noise field, providing, as a counterpart, less noise reduction capacity and, therefore, less gain in speech quality, as compared to the original adaptive BMWF algorithm. The antagonistic behavior between the signal-to-noise ratio and error associated with binaural cues indicates that it is necessary to carefully assess the tradeoff between the variables, so that designers configure appropriate parameters for each acoustic scenario

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

Speech enhancement in binaural hearing protection devices

The capability of people to operate safely and effective under extreme noise conditions is dependent on their accesses to adequate voice communication while using hearing protection. This thesis develops speech enhancement algorithms that can be implemented in binaural hearing protection devices to improve communication and situation awareness in the workplace. The developed algorithms which emphasize low computational complexity, come with the capability to suppress noise while enhancing speech

Spatial hearing rendering in wireless microphone systems for binaural hearing aids

In 2015, 360 million people, including 32 million children, were suffering from hearing impairment all over the world. This makes hearing disability a major worldwide issue. In the US, the prevalence of hearing loss increased by 160% over the past generations. However, 72% of the 34 million impaired American persons (11% of the population) still have an untreated hearing loss. Among the various current solutions alleviating hearing disability, hearing aid is the only non-invasive and the most widespread medical apparatus. Combined with hearing aids, assisting listening devices are a powerful answer to address the degraded speech understanding observed in hearing-impaired subjects, especially in noisy and reverberant environments. Unfortunately, the conventional devices do not accurately render the spatial hearing property of the human auditory system, weakening their benefits. Spatial hearing is an attribute of the auditory system relying on binaural hearing. With 2 ears, human beings are able to localize sounds in space, to get information about the acoustic surroundings, to feel immersed in environments... Furthermore, it strongly contributes to speech intelligibility. It is hypothesized that recreating an artificial spatial perception through the hearing aids of impaired people might allow for recovering a part of these subjects' hearing performance. This thesis investigates and supports the aforementioned hypothesis with both technological and clinical approaches. It reveals how certain well-established signal processing methods can be integrated in some assisting listening devices. These techniques are related to sound localization and spatialization. Taking into consideration the technical constraints of current hearing aids, as well as the characteristics of the impaired auditory system, the thesis proposes a novel solution to restore a spatial perception for users of certain types of assisting listening devices. The achieved results demonstrate the feasibility and the possible implementation of such a functionality on conventional systems. Additionally, this thesis examines the relevance and the efficiency of the proposed spatialization feature towards the enhancement of speech perception. Via a clinical trial involving a large number of patients, the artificial spatial hearing shows to be well appreciated by disabled persons, while improving or preserving their current hearing abilities. This can be considered as a prominent contribution to the current scientific and technological knowledge in the domain of hearing impairment

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