Distributed Affine Projection Algorithm Over Acoustically Coupled Sensor Networks

[EN] In this paper, we present a distributed affine projection (AP) algorithm for an acoustic sensor network where the nodes are acoustically coupled. Every acoustic node is composed of a microphone, a processor, and an actuator to control the sound field. This type of networks can use distributed adaptive algorithms to deal with the active noise control (ANC) problem in a cooperative manner, providing more flexible and scalable ANC systems. In this regard, we introduce here a distributed version of the multichannel filtered-x AP algorithm over an acoustic sensor network that it is called distributed filtered-x AP (DFxAP) algorithm. The analysis of the mean and the mean-square deviation performance of the algorithm at each node is given for a network with a ring topology and without constraints in the communication layer. The theoretical results are validated through several simulations. Moreover, simulations show that the proposed DFxAP outperforms the previously reported distributed multiple error filtered-x least mean square algorithm.This work was supported in part by EU together with Spanish Government under Grant TEC2015-67387-C4-1-R (MINECO/FEDER), and in part by Generalitat Valenciana under PROMETEOII/2014/003.Ferrer Contreras, M.; Gonzalez, A.; Diego Antón, MD.; Piñero, G. (2017). Distributed Affine Projection Algorithm Over Acoustically Coupled Sensor Networks. IEEE Transactions on Signal Processing. 65(24):6423-6434. https://doi.org/10.1109/TSP.2017.2742987S64236434652

Distributed and Collaborative Processing of Audio Signals: Algorithms, Tools and Applications

Tesis por compendio[ES] Esta tesis se enmarca en el campo de las Tecnologías de la Información y las Comunicaciones (TIC), especialmente en el área del procesado digital de la señal. En la actualidad, y debido al auge del Internet de los cosas (IoT), existe un creciente interés por las redes de sensores inalámbricos (WSN), es decir, redes compuestas de diferentes tipos de dispositivos específicamente distribuidos en una determinada zona para realizar diferentes tareas de procesado de señal. Estos dispositivos o nodos suelen estar equipados con transductores electroacústicos así como con potentes y eficientes procesadores con capacidad de comunicación. En el caso particular de las redes de sensores acústicos (ASN), los nodos se dedican a resolver diferentes tareas de procesado de señales acústicas. El desarrollo de potentes sistemas de procesado centralizado han permitido aumentar el número de canales de audio, ampliar el área de control o implementar algoritmos más complejos. En la mayoría de los casos, una topología de ASN distribuida puede ser deseable debido a varios factores tales como el número limitado de canales utilizados por los dispositivos de adquisición y reproducción de audio, la conveniencia de un sistema escalable o las altas exigencias computacionales de los sistemas centralizados. Todos estos aspectos pueden llevar a la utilización de nuevas técnicas de procesado distribuido de señales con el fin de aplicarlas en ASNs. Para ello, una de las principales aportaciones de esta tesis es el desarrollo de algoritmos de filtrado adaptativo para sistemas de audio multicanal en redes distribuidas. Es importante tener en cuenta que, para aplicaciones de control del campo sonoro (SFC), como el control activo de ruido (ANC) o la ecualización activa de ruido (ANE), los nodos acústicos deben estar equipados con actuadores con el fin de controlar y modificar el campo sonoro. Sin embargo, la mayoría de las propuestas de redes distribuidas adaptativas utilizadas para resolver problemas de control del campo sonoro no tienen en cuenta que los nodos pueden interferir o modificar el comportamiento del resto. Por lo tanto, otra contribución destacable de esta tesis se centra en el análisis de cómo el sistema acústico afecta el comportamiento de los nodos dentro de una ASN. En los casos en que el entorno acústico afecta negativamente a la estabilidad del sistema, se han propuesto varias estrategias distribuidas para resolver el problema de interferencia acústica con el objetivo de estabilizar los sistemas de ANC. En el diseño de los algoritmos distribuidos también se han tenido en cuenta aspectos de implementación práctica. Además, con el objetivo de crear perfiles de ecualización diferentes en zonas de escucha independientes en presencia de ruidos multitonales, se han presentado varios algoritmos distribuidos de ANE en banda estrecha y banda ancha sobre una ASN con una comunicación colaborativa y compuesta por nodos acústicos. Se presentan además resultados experimentales para validar el uso de los algoritmos distribuidos propuestos en el trabajo para aplicaciones prácticas. Para ello, se ha diseñado un software de simulación acústica que permite analizar el rendimiento de los algoritmos desarrollados en la tesis. Finalmente, se ha realizado una implementación práctica que permite ejecutar aplicaciones multicanal de SFC. Para ello, se ha desarrollado un prototipo en tiempo real que controla las aplicaciones de ANC y ANE utilizando nodos acústicos colaborativos. El prototipo consiste en dos sistemas de control de audio personalizado (PAC) compuestos por un asiento de coche y un nodo acústico, el cual está equipado con dos altavoces, dos micrófonos y un procesador con capacidad de comunicación entre los dos nodos. De esta manera, es posible crear dos zonas independientes de control de ruido que mejoran el confort acústico del usuario sin necesidad de utilizar auriculares.[CA] Aquesta tesi s'emmarca en el camp de les Tecnologies de la Informació i les Comunicacions (TIC), especialment en l'àrea del processament digital del senyal. En l'actualitat, i a causa de l'auge de la Internet dels coses (IoT), existeix un creixent interés per les xarxes de sensors sense fils (WSN), és a dir, xarxes compostes de diferents tipus de dispositius específicament distribuïts en una determinada zona per a fer diferents tasques de processament de senyal. Aquests dispositius o nodes solen estar equipats amb transductors electroacústics així com amb potents i eficients processadors amb capacitat de comunicació. En el cas particular de les xarxes de sensors acústics (ASN), els nodes es dediquen a resoldre diferents tasques de processament de senyals acústics. El desenvolupament de potents sistemes de processament centralitzat han permés augmentar el nombre de canals d'àudio, ampliar l'àrea de control o implementar algorismes més complexos. En la majoria dels casos, una topologia de ASN distribuïda pot ser desitjable a causa de diversos factors tals com el nombre limitat de canals utilitzats pels dispositius d'adquisició i reproducció d'àudio, la conveniència d'un sistema escalable o les altes exigències computacionals dels sistemes centralitzats. Tots aquests aspectes poden portar a la utilització de noves tècniques de processament distribuït de senyals amb la finalitat d'aplicar-les en ASNs. Per a això, una de les principals aportacions d'aquesta tesi és el desenvolupament d'algorismes de filtrat adaptatiu per a sistemes d'àudio multicanal en xarxes distribuïdes. És important tindre en compte que, per a aplicacions de control del camp sonor (SFC), com el control actiu de soroll (ANC) o l'equalització activa de soroll (ANE), els nodes acústics han d'estar equipats amb actuadors amb la finalitat de controlar i modificar el camp sonor. No obstant això, la majoria de les propostes de xarxes distribuïdes adaptatives utilitzades per a resoldre problemes de control del camp sonor no tenen en compte que els nodes poden modificar el comportament de la resta. Per tant, una altra contribució destacable d'aquesta tesi se centra en l'anàlisi de com el sistema acústic afecta el comportament dels nodes dins d'una ASN. En els casos en què l'entorn acústic afecta negativament a l'estabilitat del sistema, s'han proposat diverses estratègies distribuïdes per a resoldre el problema d'interferència acústica amb l'objectiu d'estabilitzar els sistemes de ANC. En el disseny dels algorismes distribuïts també s'han tingut en compte aspectes d'implementació pràctica. A més, amb l'objectiu de crear perfils d'equalització diferents en zones d'escolta independents en presència de sorolls multitonales, s'han presentat diversos algorismes distribuïts de ANE en banda estreta i banda ampla sobre una ASN amb una comunicació col·laborativa i composta per nodes acústics. Es presenten a més resultats experimentals per a validar l'ús dels algorismes distribuïts proposats en el treball per a aplicacions pràctiques. Per a això, s'ha dissenyat un programari de simulació acústica que permet analitzar el rendiment dels algorismes desenvolupats en la tesi. Finalment, s'ha realitzat una implementació pràctica que permet executar aplicacions multicanal de SFC. Per a això, s'ha desenvolupat un prototip en temps real que controla les aplicacions de ANC i ANE utilitzant nodes acústics col·laboratius. El prototip consisteix en dos sistemes de control d'àudio personalitzat (PAC) compostos per un seient de cotxe i un node acústic, el qual està equipat amb dos altaveus, dos micròfons i un processador amb capacitat de comunicació entre els dos nodes. D'aquesta manera, és possible crear dues zones independents de control de soroll que milloren el confort acústic de l'usuari sense necessitat d'utilitzar auriculars.[EN] This thesis fits into the field of Information and Communications Technology (ICT), especially in the area of digital signal processing. Nowadays and due to the rise of the Internet of Things (IoT), there is a growing interest in wireless sensor networks (WSN), that is, networks composed of different types of devices specifically distributed in some area to perform different signal processsing tasks. These devices, also referred to as nodes, are usually equipped with electroacoustic transducers as well as powerful and efficient processors with communication capability. In the particular case of acoustic sensor networks (ASN), nodes are dedicated to solving different acoustic signal processing tasks. These audio signal processing applications have been undergone a major development in recent years due in part to the advances made in computer hardware and software. The development of powerful centralized processing systems has allowed the number of audio channels to be increased, the control area to be extended or more complex algorithmms to be implemented. In most cases, a distributed ASN topology can be desirable due to several factors such as the limited number of channels used by the sound acquisition and reproduction devices, the convenience of a scalable system or the high computational demands of a centralized fashion. All these aspects may lead to the use of novel distributed signal processing techniques with the aim to be applied over ASNs. To this end, one of the main contributions of this dissertation is the development of adaptive filtering algorithms for multichannel sound systems over distributed networks. Note that, for sound field control (SFC) applications, such as active noise control (ANC) or active noise equalization (ANE), acoustic nodes must be not only equipped with sensors but also with actuators in order to control and modify the sound field. However, most of the adaptive distributed networks approaches used to solve soundfield control problems do not take into account that the nodes may interfere or modify the behaviour of the rest. Therefore, other important contribution of this thesis is focused on analyzing how the acoustic system affects the behavior of the nodes within an ASN. In cases where the acoustic environment adversely affects the system stability, several distributed strategies have been proposed for solving the acoustic interference problem with the aim to stabilize ANC control systems. These strategies are based on both collaborative and non-collaborative approaches. Implementation aspects such as hardware constraints, sensor locations, convergenge rate or computational and communication burden, have been also considered on the design of the distributed algorithms. Moreover and with the aim to create independent-zone equalization profiles in the presence of multi-tonal noises, distributed narrowband and broadband ANE algorithms over an ASN with a collaborative learning and composed of acoustic nodes have been presented. Experimental results are presented to validate the use of the distributed algorithms proposed in the work for practical applications. For this purpose, an acoustic simulation software has been specifically designed to analyze the performance of the developed algorithms. Finally, the performance of the proposed distributed algorithms for multichannel SFC applications has been evaluated by means of a real practical implementation. To this end, a real-time prototype that controls both ANC and ANE applications by using collaborative acoustic nodes has been developed. The prototype consists of two personal audio control (PAC) systems composed of a car seat and an acoustic node, which is equipped with two loudspeakers, two microphones and a processor with communications capability. In this way, it is possible to create two independent noise control zones improving the acoustic comfort of the user without the use of headphones.Antoñanzas Manuel, C. (2019). Distributed and Collaborative Processing of Audio Signals: Algorithms, Tools and Applications [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/130209TESISCompendi

ROBUSTNESS ANALYSIS OF THE DATA-SELECTIVE VOLTERRA NLMS ALGORITHM

Recently, the data-selective adaptive Volterra filters have been proposed;however, up to now, there are not any theoretical analyses on its behavior rather than numerical simulations. Therefore, in this paper, we analyze the robustness (in the sense of l_2-stability) of the data-selective Volterra normalized least-mean-square (DSVNLMS) algorithm. First, we study the local robustness of this algorithm at any iteration, then we propose a global bound for the error/discrepancy in the coefficient vector. Also, we demonstrate that the DS-VNLMS algorithm improves the parameter estimation for the majority of the iterations that an update is implemented. Moreover, we also prove that if the noise bound is known, then we can set the DS-VNLMS so that it never degrades the estimate. The simulation results corroborate the validity of the executed analysis and demonstrate that the DS-VNLMS algorithm is robust against noise, no matter how its parameters are adopted

On data-selective learning

Adaptive filters are applied in several electronic and communication devices like smartphones, advanced headphones, DSP chips, smart antenna, and teleconference systems. Also, they have application in many areas such as system identification, channel equalization, noise reduction, echo cancellation, interference cancellation, signal prediction, and stock market. Therefore, reducing the energy consumption of the adaptive filtering algorithms has great importance, particularly in green technologies and in devices using battery. In this thesis, data-selective adaptive filters, in particular the set-membership (SM) adaptive filters, are the tools to reach the goal. There are well known SM adaptive filters in literature. This work introduces new algorithms based on the classical ones in order to improve their performances and reduce the number of required arithmetic operations at the same time. Therefore, firstly, we analyze the robustness of the classical SM adaptive filtering algorithms. Secondly, we extend the SM technique to trinion and quaternion systems. Thirdly, by combining SM filtering and partialupdating, we introduce a new improved set-membership affine projection algorithm with constrained step size to improve its stability behavior. Fourthly, we propose some new least-mean-square (LMS) based and recursive least-squares based adaptive filtering algorithms with low computational complexity for sparse systems. Finally, we derive some feature LMS algorithms to exploit the hidden sparsity in the parameters.Filtros adaptativos são aplicados em diversos aparelhos eletrônicos e de comunicação, como smartphones, fone de ouvido avançados, DSP chips, antenas inteligentes e sistemas de teleconferência. Eles também têm aplicação em várias áreas como identificação de sistemas, equalização de canal, cancelamento de eco, cancelamento de interferência, previsão de sinal e mercado de ações. Desse modo, reduzir o consumo de energia de algoritmos adaptativos tem importância significativa, especialmente em tecnologias verdes e aparelhos que usam bateria. Nesta tese, filtros adaptativos com seleção de dados, em particular filtros adaptativos da família set-membership (SM), são apresentados para cumprir essa missão. No presente trabalho objetivamos apresentar novos algoritmos, baseados nos clássicos, a fim de aperfeiçoar seus desempenhos e, ao mesmo tempo, reduzir o número de operações aritméticas exigidas. Dessa forma, primeiro analisamos a robustez dos filtros adaptativos SM clássicos. Segundo, estendemos o SM aos números trinions e quaternions. Terceiro, foram utilizadas também duas famílias de algoritmos, SM filtering e partial-updating, de uma maneira elegante, visando reduzir energia ao máximo possível e obter um desempenho competitivo em termos de estabilidade. Quarto, a tese propõe novos filtros adaptativos baseado em algoritmos least-mean-square (LMS) e mínimos quadrados recursivos com complexidade computacional baixa para espaços esparsos. Finalmente, derivamos alguns algoritmos feature LMS para explorar a esparsidade escondida nos parâmetros

Subband Adaptive Modeling of Digital Hearing Aids

In this thesis, the application of a subband adaptive model to characterize compression behaviour of five digital hearing aids is investigated. Using a signal-to-error ratio metric, modeling performance is determined by varying the number of analysis bands in the subband structure as well as consideration of three adaptive algorithms. The normalized least mean-squares (NLMS), the affine projection algorithm (APA), and the recursive least-squares (RLS) algorithms are employed using a range of parameters to determine the impact on modeling performance. Using the subband adaptive model to estimate the time-varying frequency response of each hearing aid allows the Perceptual Evaluation of Speech Quality (PESQ) mean-opinion score (MOS) to be computed. The PESQ MOS facilitates an estimation of a subjective assessment of speech quality using an objective score. Initial results suggest the PESQ MOS score is able to differentiate speech processed by hearing aids allowing them to be ranked accordingly. Further work is required to obtain subjective assessments of the processed speech signals and determine if possible correlations exist

A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

Analysis of Transient and Steady-State Behavior of a Multichannel Filtered-<it>x</it> Partial-Error Affine Projection Algorithm

The paper provides an analysis of the transient and the steady-state behavior of a filtered-x partial-error affine projection algorithm suitable for multichannel active noise control. The analysis relies on energy conservation arguments, it does not apply the independence theory nor does it impose any restriction to the signal distributions. The paper shows that the partial-error filtered-x affine projection algorithm in presence of stationary input signals converges to a cyclostationary process, that is, the mean value of the coefficient vector, the mean-square error and the mean-square deviation tend to periodic functions of the sample time.</p

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems