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

Affine Projection Algorithm Over Acoustic Sensor Networks for Active Noise Control

[EN] Acoustic sensor networks (ASNs) are an effective solution to implement active noise control (ANC) systems by using distributed adaptive algorithms. On one hand, ASNs provide scalable systems where the signal processing load is distributed among the network nodes. On the other hand, their noise reduction performance is comparable to that of their respective centralized processing systems. In this sense, the distributed multiple error filtered-x least mean squares (DMEFxLMS) adaptive algorithm has shown to obtain the same performance than its centralized counterpart as long as there are no communications constraints in the underlying ASN. Regarding affine projection (AP) adaptive algorithms, some distributed approaches that are approximated versions of the multichannel filtered-x affine projection (MFxAP) algorithm have been previously proposed. These AP algorithms can efficiently share the processing load among the nodes, but at the expense of worsening their convergence properties. In this paper we develop the exact distributed multichannel filtered-x AP (EFxAP) algorithm, which obtains the same solution as that of the MFxAP algorithm as long as there are no communications constraints in the underlying ASN. In the EFxAP algorithm each node can compute a part or the entire inverse matrix needed by the centralized MFxAP algorithm. Thus, we propose three different strategies that obtain significant computational saving: 1) Gauss Elimination, 2) block LU factorization, and 3) matrix inversion lemma. As a result, each node computes only between 25%¿60% of the number of multiplications required by the direct inversion of the matrix. Regarding the performance in transient and steady states, the EFxAP exhibits the fastest convergence and the highest noise level reduction for any size of the acoustic network and any projection order of the AP algorithm compared to the DMEFxLMS and two previously reported distributed AP algorithms.This work was supported by EU together with Spanish Government through RTI2018-098085B-C41 (MINECO/FEDER) and Generalitat Valenciana through PROMETEO/2019/109.Ferrer Contreras, M.; Diego Antón, MD.; Piñero, G.; Gonzalez, A. (2021). Affine Projection Algorithm Over Acoustic Sensor Networks for Active Noise Control. IEEE/ACM Transactions on Audio Speech and Language Processing. 29:448-461. https://doi.org/10.1109/TASLP.2020.3042590S4484612

Fast exact variable order affine projection algorithm

Variable order affine projection algorithms have been recently presented to be used when not only the convergence speed of the algorithm has to be adjusted but also its computational cost and its final residual error. These kind of affine projection (AP) algorithms improve the standard AP algorithm performance at steady state by reducing the residual mean square error. Furthermore these algorithms optimize computational cost by dynamically adjusting their projection order to convergence speed requirements. The main cost of the standard AP algorithm is due to the matrix inversion that appears in the coefficient update equation. Most efforts to decrease the computational cost of these algorithms have focused on the optimization of this matrix inversion. This paper deals with optimization of the computational cost of variable order AP algorithms by recursive calculation of the inverse signal matrix. Thus, a fast exact variable order AP algorithm is proposed. Exact iterative expressions to calculate the inverse matrix when the algorithm projection order either increases or decreases are incorporated into a variable order AP algorithm leading to a reduced complexity implementation. The simulation results show the proposed algorithm performs similarly to the variable order AP algorithms and it has a lower computational complexity. © 2012 Elsevier B.V. All rights reserved.Partially supported by TEC2009-13741, PROMETEO 2009/0013, GV/ 2010/027, ACOMP/2010/006 and UPV PAID-06-09.Ferrer Contreras, M.; Gonzalez, A.; Diego Antón, MD.; Piñero Sipán, MG. (2012). Fast exact variable order affine projection algorithm. Signal Processing. 92(9):2308-2314. https://doi.org/10.1016/j.sigpro.2012.03.007S2308231492

Sparseness-controlled adaptive algorithms for supervised and unsupervised system identification

In single-channel hands-free telephony, the acoustic coupling between the loudspeaker and the microphone can be strong and this generates echoes that can degrade user experience. Therefore, effective acoustic echo cancellation (AEC) is necessary to maintain a stable system and hence improve the perceived voice quality of a call. Traditionally, adaptive filters have been deployed in acoustic echo cancellers to estimate the acoustic impulse responses (AIRs) using adaptive algorithms. The performances of a range of well-known algorithms are studied in the context of both AEC and network echo cancellation (NEC). It presents insights into their tracking performances under both time-invariant and time-varying system conditions. In the context of AEC, the level of sparseness in AIRs can vary greatly in a mobile environment. When the response is strongly sparse, convergence of conventional approaches is poor. Drawing on techniques originally developed for NEC, a class of time-domain and a frequency-domain AEC algorithms are proposed that can not only work well in both sparse and dispersive circumstances, but also adapt dynamically to the level of sparseness using a new sparseness-controlled approach. As it will be shown later that the early part of the acoustic echo path is sparse while the late reverberant part of the acoustic path is dispersive, a novel approach to an adaptive filter structure that consists of two time-domain partition blocks is proposed such that different adaptive algorithms can be used for each part. By properly controlling the mixing parameter for the partitioned blocks separately, where the block lengths are controlled adaptively, the proposed partitioned block algorithm works well in both sparse and dispersive time-varying circumstances. A new insight into an analysis on the tracking performance of improved proportionate NLMS (IPNLMS) is presented by deriving the expression for the mean-square error. By employing the framework for both sparse and dispersive time-varying echo paths, this work validates the analytic results in practical simulations for AEC. The time-domain second-order statistic based blind SIMO identification algorithms, which exploit the cross relation method, are investigated and then a technique with proportionate step-size control for both sparse and dispersive system identification is also developed

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

Efficient Multiband Algorithms for Blind Source Separation

The problem of blind separation refers to recovering original signals, called source signals, from the mixed signals, called observation signals, in a reverberant environment. The mixture is a function of a sequence of original speech signals mixed in a reverberant room. The objective is to separate mixed signals to obtain the original signals without degradation and without prior information of the features of the sources. The strategy used to achieve this objective is to use multiple bands that work at a lower rate, have less computational cost and a quicker convergence than the conventional scheme. Our motivation is the competitive results of unequal-passbands scheme applications, in terms of the convergence speed. The objective of this research is to improve unequal-passbands schemes by improving the speed of convergence and reducing the computational cost. The first proposed work is a novel maximally decimated unequal-passbands scheme.This scheme uses multiple bands that make it work at a reduced sampling rate, and low computational cost. An adaptation approach is derived with an adaptation step that improved the convergence speed. The performance of the proposed scheme was measured in different ways. First, the mean square errors of various bands are measured and the results are compared to a maximally decimated equal-passbands scheme, which is currently the best performing method. The results show that the proposed scheme has a faster convergence rate than the maximally decimated equal-passbands scheme. Second, when the scheme is tested for white and coloured inputs using a low number of bands, it does not yield good results; but when the number of bands is increased, the speed of convergence is enhanced. Third, the scheme is tested for quick changes. It is shown that the performance of the proposed scheme is similar to that of the equal-passbands scheme. Fourth, the scheme is also tested in a stationary state. The experimental results confirm the theoretical work. For more challenging scenarios, an unequal-passbands scheme with over-sampled decimation is proposed; the greater number of bands, the more efficient the separation. The results are compared to the currently best performing method. Second, an experimental comparison is made between the proposed multiband scheme and the conventional scheme. The results show that the convergence speed and the signal-to-interference ratio of the proposed scheme are higher than that of the conventional scheme, and the computation cost is lower than that of the conventional scheme

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications