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

Active noise control over adaptive distributed networks

© 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper presents the implementation of Active Noise Control (ANC) systems over a network of distributed acoustic nodes. For this purpose we define a general acoustic node consisting of one or several microphones and one or several loudspeakers together with a unique processor with communication capabilities. ANC systems can use a wide range of adaptive algorithms, but we have considered specifically the Multiple Error Filtered-x Least Mean Square (MEFxLMS), which has been proved to perform very well for ANC systems with multiple microphones and loudspeakers, and centralized processing. We present a new formulation to introduce the distributed version of the MEFxLMS together with an incremental collaborative strategy in the network. We demonstrate that the distributed MEFxLMS exhibits the same performance as the centralized one when there are no communication constraints in the network. Then, we re-formulate the distributed MEFxLMS to include parameters related to its implementation on an acoustic sensor network: latency of the network, computational capacity of the nodes, and trustworthiness of the signals measured at each node. Simulation results in realistic scenarios show the ability of the proposed distributed algorithms to achieve good performance when proper values of these parameters are chosen. (C) 2014 Elsevier B.V. All rights reserved.This work has been supported by European Union ERDF and Spanish Government through TEC2012-38142-C04 Project, and Generalitat Valenciana through PROMETEOII/2014/003 Project.Ferrer Contreras, M.; Diego Antón, MD.; Piñero Sipán, MG.; González Salvador, A. (2015). Active noise control over adaptive distributed networks. Signal Processing. 107:82-95. https://doi.org/10.1016/j.sigpro.2014.07.026S829510

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

Distributed Active Noise Control System Based on a Block Diffusion FxLMS Algorithm with Bidirectional Communication

Recently, distributed active noise control systems based on diffusion adaptation have attracted significant research interest due to their balance between computational complexity and stability compared to conventional centralized and decentralized adaptation schemes. However, the existing diffusion FxLMS algorithm employs node-specific adaptation and neighborhood-wide combination, and assumes that the control filters of neighbor nodes are similar to each other. This assumption is not true in practical applications, and it leads to inferior performance to the centralized controller approach. In contrast, this paper proposes a Block Diffusion FxLMS algorithm with bidirectional communication, which uses neighborhood-wide adaptation and node-specific combination to update the control filters. Simulation results validate that the proposed algorithm converges to the solution of the centralized controller with reduced computational burden

Control Effort Strategies for Acoustically Coupled Distributed Acoustic Nodes

[EN] This paper considers the effect of effort constraints on the behavior of an active noise control (ANC) system over a distributed network composed of acoustic nodes. A distributed implementation can be desirable in order to provide more flexible, versatile, and scalable ANC systems. In this regard, the distributed version of the multiple error filtered-x least mean square (DMEFxLMS) algorithm that allows collaboration between nodes has shown excellent properties. However, practical constraints need to be considered since, in real scenarios, the acoustic nodes are equipped with power constrained actuators. If these constraints are not considered within the adaptive algorithm, the control signals may increase and saturate the hardware devices, causing system instability. To avoid this drawback, a control effort weighting can be considered in the cost function of the distributed algorithm at each node. Therefore, a control effort strategy over the output signals at each node is used to keep them under a given threshold and ensuring the distributed ANC system stability. Experimental results show that, assuming ideal network communications, the proposed distributed algorithm achieves the same performance as the leaky centralized ANC system. A performance evaluation of several versions of the leaky DMEFxLMS algorithm in realistic scenarios is also included.This work has been supported by European Union ERDF together with Spanish Government through TEC2015-67387-C4-1-R project and Generalitat Valenciana through PROMETEOII/2014/003 project.Antoñanzas-Manuel, C.; Ferrer Contreras, M.; Diego Antón, MD.; Gonzalez, A. (2017). Control Effort Strategies for Acoustically Coupled Distributed Acoustic Nodes. Wireless Communications and Mobile Computing. 2017:1-15. https://doi.org/10.1155/2017/3601802S1152017Akyildiz, I. F., Weilian Su, Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102-114. doi:10.1109/mcom.2002.1024422Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292-2330. doi:10.1016/j.comnet.2008.04.002Puccinelli, D., & Haenggi, M. (2005). Wireless sensor networks: applications and challenges of ubiquitous sensing. IEEE Circuits and Systems Magazine, 5(3), 19-31. doi:10.1109/mcas.2005.1507522Xiaojiang Du, & Hsiao-Hwa Chen. (2008). Security in wireless sensor networks. IEEE Wireless Communications, 15(4), 60-66. doi:10.1109/mwc.2008.4599222Al Ameen, M., Liu, J., & Kwak, K. (2010). Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications. Journal of Medical Systems, 36(1), 93-101. doi:10.1007/s10916-010-9449-4Martinez, K., Hart, J. K., & Ong, R. (2004). Environmental sensor networks. Computer, 37(8), 50-56. doi:10.1109/mc.2004.91Segura-Garcia, J., Felici-Castell, S., Perez-Solano, J. J., Cobos, M., & Navarro, J. M. (2015). Low-Cost Alternatives for Urban Noise Nuisance Monitoring Using Wireless Sensor Networks. IEEE Sensors Journal, 15(2), 836-844. doi:10.1109/jsen.2014.2356342Flammini, A., Ferrari, P., Marioli, D., Sisinni, E., & Taroni, A. (2009). Wired and wireless sensor networks for industrial applications. Microelectronics Journal, 40(9), 1322-1336. doi:10.1016/j.mejo.2008.08.012Lopes, C. G., & Sayed, A. H. (2007). Incremental Adaptive Strategies Over Distributed Networks. IEEE Transactions on Signal Processing, 55(8), 4064-4077. doi:10.1109/tsp.2007.896034Cobos, M., Perez-Solano, J. J., Belmonte, O., Ramos, G., & Torres, A. M. (2016). Simultaneous Ranging and Self-Positioning in Unsynchronized Wireless Acoustic Sensor Networks. IEEE Transactions on Signal Processing, 64(22), 5993-6004. doi:10.1109/tsp.2016.2603972Llerena-Aguilar, C., Gil-Pita, R., Rosa-Zurera, M., Ayllón, D., Utrilla-Manso, M., & Llerena, F. (2016). Synchronization based on mixture alignment for sound source separation in wireless acoustic sensor networks. Signal Processing, 118, 177-187. doi:10.1016/j.sigpro.2015.06.023Elliott, S. J., & Nelson, P. A. (1993). Active noise control. IEEE Signal Processing Magazine, 10(4), 12-35. doi:10.1109/79.248551Elliott, S. J., Joseph, P., Bullmore, A. J., & Nelson, P. A. (1988). Active cancellation at a point in a pure tone diffuse sound field. Journal of Sound and Vibration, 120(1), 183-189. doi:10.1016/0022-460x(88)90343-4Joseph, P., Elliott, S. J., & Nelson, P. A. (1994). Near Field Zones of Quiet. Journal of Sound and Vibration, 172(5), 605-627. doi:10.1006/jsvi.1994.1202Kuo, S. M., & Morgan, D. R. (1999). Active noise control: a tutorial review. Proceedings of the IEEE, 87(6), 943-975. doi:10.1109/5.763310Burgess, J. C. (1981). Active adaptive sound control in a duct: A computer simulation. The Journal of the Acoustical Society of America, 70(3), 715-726. doi:10.1121/1.386908Elliott, S. J., & Boucher, C. C. (1994). Interaction between multiple feedforward active control systems. IEEE Transactions on Speech and Audio Processing, 2(4), 521-530. doi:10.1109/89.326611Grosdidier, P., & Morari, M. (1986). Interaction measures for systems under decentralized control. Automatica, 22(3), 309-319. doi:10.1016/0005-1098(86)90029-4Elliott, S., Stothers, I., & Nelson, P. (1987). A multiple error LMS algorithm and its application to the active control of sound and vibration. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(10), 1423-1434. doi:10.1109/tassp.1987.1165044Elliott, S. J., & Back, K. H. (1996). Effort constraints in adaptive feedforward control. IEEE Signal Processing Letters, 3(1), 7-9. doi:10.1109/97.475821Qiu, X., & Hansen, C. H. (2001). A study of time-domain FXLMS algorithms with control output constraint. The Journal of the Acoustical Society of America, 109(6), 2815-2823. doi:10.1121/1.1367247Rafaely, B., & Elliot, S. J. (2000). A computationally efficient frequency-domain LMS algorithm with constraints on the adaptive filter. IEEE Transactions on Signal Processing, 48(6), 1649-1655. doi:10.1109/78.845922Kozacky, W. J., & Ogunfunmi, T. (2013). An active noise control algorithm with gain and power constraints on the adaptive filter. EURASIP Journal on Advances in Signal Processing, 2013(1). doi:10.1186/1687-6180-2013-17Mosquera-Sánchez, J. A., Desmet, W., & de Oliveira, L. P. R. (2017). A multichannel amplitude and relative-phase controller for active sound quality control. Mechanical Systems and Signal Processing, 88, 145-165. doi:10.1016/j.ymssp.2016.10.036Rossetti, D. J., Jolly, M. R., & Southward, S. C. (1996). Control effort weighting in feedforward adaptive control systems. The Journal of the Acoustical Society of America, 99(5), 2955-2964. doi:10.1121/1.414877Antoñanzas, C., Ferrer, M., de Diego, M., & Gonzalez, A. (2016). Blockwise Frequency Domain Active Noise Controller Over Distributed Networks. Applied Sciences, 6(5), 124. doi:10.3390/app605012

Blockwise Frequency Domain Active Noise Controller Over Distributed Networks

© 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).This work presents a practical active noise control system composed of distributed and collaborative acoustic nodes. To this end, experimental tests have been carried out in a listening room with acoustic nodes equipped with loudspeakers and microphones. The communication among the nodes is simulated by software. We have considered a distributed algorithm based on the Filtered-x Least Mean Square (FxLMS) method that introduces collaboration between nodes following an incremental strategy. For improving the processing efficiency in practical scenarios where data acquisition systems work by blocks of samples, the frequency-domain partitioned block technique has been used. Implementation aspects such as computational complexity, processing time of the network and convergence of the algorithm have been analyzed. Experimental results show that, without constraints in the network communications, the proposed distributed algorithm achieves the same performance as the centralized version. The performance of the proposed algorithm over a network with a given communication delay is also included.This work has been supported by the European Union (European Regional Development Fund) together with Spanish Government through TEC2015-67387-C4-1-R project, the grant BES-2013-063783 and Generalitat Valenciana through the PROMETEOII/2014/003 project.Antoñanzas-Manuel, C.; Ferrer Contreras, M.; Diego Antón, MD.; Gonzalez, A. (2016). Blockwise Frequency Domain Active Noise Controller Over Distributed Networks. Applied Sciences. 6(5). https://doi.org/10.3390/app6050124S1246

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 Coupled Multi-Agent Stochastic Optimization

This work develops effective distributed strategies for the solution of constrained multi-agent stochastic optimization problems with coupled parameters across the agents. In this formulation, each agent is influenced by only a subset of the entries of a global parameter vector or model, and is subject to convex constraints that are only known locally. Problems of this type arise in several applications, most notably in disease propagation models, minimum-cost flow problems, distributed control formulations, and distributed power system monitoring. This work focuses on stochastic settings, where a stochastic risk function is associated with each agent and the objective is to seek the minimizer of the aggregate sum of all risks subject to a set of constraints. Agents are not aware of the statistical distribution of the data and, therefore, can only rely on stochastic approximations in their learning strategies. We derive an effective distributed learning strategy that is able to track drifts in the underlying parameter model. A detailed performance and stability analysis is carried out showing that the resulting coupled diffusion strategy converges at a linear rate to an $O(\mu)-$neighborhood of the true penalized optimizer

Shaping zones of quiet in a large enclosure generated by an active noise control system

[EN] Performance of an Active Noise Control (ANC) system strongly depends on sensors and actuators spatial arrangement. It determines both achieved Noise Reduction (NR) levels and spatial distribution of obtained zones of quiet, making it an essential problem. However, if the acoustic field in the enclosure can be appropriately modelled, then optimization algorithms can be employed to find efficient configuration of the ANC system, enhancing its performance according to a formulated cost function and constraints. This paper proposes a complete method for enhancing NR levels and shaping zones of quiet generated with an ANC system by optimization of sensors and actuators arrangement. A Memetic Algorithm (MA) is utilized. The MA itself and its proposed operators are described. The optimization problem formulation is derived and discussed. As a control algorithm, Distributed Multiple Error Filtered-x Least Mean Square (DMEFxLMS) is used. Extensive simulation results are presented for an exemplary real enclosure. The model of the acoustic environment has been obtained by real-world experiments, resulting in identification of 36864 acoustic responses in total. Practically feasible cost function and constraints are evaluated. Advantages and limits of the method are pointed out and discussed.The research reported in this paper has been supported by the National Science Centre, Poland, decision no. DEC-2017/25/B/ST7/02236, and by EU together with Spanish Government under Grant TEC2015-67387-C4-1-R b(MINECO/FEDER).Wrona, S.; Diego Antón, MD.; Pawelczyk, M. (2018). Shaping zones of quiet in a large enclosure generated by an active noise control system. Control Engineering Practice. 80:1-16. https://doi.org/10.1016/j.conengprac.2018.08.004S1168