Filtrado adaptativo multicanal para control local de campo sonoro basado en algoritmos de proyección afín

Esta Tesis doctoral se ha centrado en el desarrollo e implementación de algoritmos eficientes multicanal, basados en el algoritmo de proyección afín, aplicados al control activo de ruido. Para abordar esta cuestión primeramente se han estudiado diferentes algoritmos eficientes de proyección afín que han sido analizados y validados mediante simulación, finalizando con la implementación, en un recinto, de un sistema real de control activo de ruido multicanal ejecutado en un DSP controlado por dichos algoritmos. En los últimos años, los algoritmos de proyección afín han sido propuestos como algoritmos de control en sistemas adaptativos, que pretenden mejorar la velocidad de convergencia de los algoritmos basados en el LMS, siendo una alternativa eficiente, robusta y estable frente a estos algoritmos, cuya limitación principal es, precisamente, la velocidad de convergencia. Los algoritmos de proyección afín pueden ser considerados como una extensión natural del algoritmo NLMS, ya que éste actualiza sus coeficientes basándose en un único vector de datos de la señal de entrada mientras que los algoritmos de proyección afín actualizan los coeficientes de los filtros adaptativos usando N vectores de datos de la señal de entrada (siendo N el orden de proyección). Se han dedicado muchos esfuerzos para tratar de optimizar la eficiencia computacional de estos algoritmos aplicados al problema de la cancelación de eco, surgiendo diferentes versiones eficientes del algoritmo de proyección afín. Sin embargo, al aplicarlo al control activo de ruido, es necesario reducir aún más la complejidad computacional, teniendo en cuenta que, por lo general, la eficiencia computacional se consigue a costa de la degradación de alguna otra característica del algoritmo (generalmente la velocidad de convergencia). En este trabajo se presentan algunas alternativas a versiones eficientes existentes, que no degradan significativamente las prestaciones de dicho algoritmo, y se analiza cómo reducir aúnFerrer Contreras, M. (2008). Filtrado adaptativo multicanal para control local de campo sonoro basado en algoritmos de proyección afín [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3796Palanci

Subjective perception of effort, fatigue and degree of technical difficulty in swimmers with and without Asperger Syndrome

[ES] El entrenamiento con discapacitados intelectuales no ha sido suficientemente estudiado, siendo necesario conocer esta temática para muchos profesionales. Este trabajo pretende saber más sobre percepción del esfuerzo, fatiga y grado de dificultad técnica que presentan nadadores con y sin Síndrome de Asperger. Para ello se realizó un test de 8x100 metros libres progresivos finalizando a intensidad máxima. Se utilizó la escala de Borg para conocer las percepciones de esfuerzo y fatiga y una pregunta sobre el grado de dificultad técnica, tanto en nadadores Asperger como no. De los tres aspectos estudiados, no se aprecian diferencias de percepción en esfuerzo y fatiga, no ocurriendo lo mismo con el grado de dificultad técnica, donde los nadadores Asperger muestran dificultad para diferenciar la dificultad técnica real, considerando todos los ejercicios de la misma dificultad e imposibilitando un valor definido que determine el grado de correlación de sus respuestas con la dificultad real objetiva.[EN] The training focus to the intellectual disabled people has not been studied enough, even though that professionals need to know more about this topic. The main aim of this work is to know better how swimmers with and without Asperger Syndrome perceive the effort, fatigue, and the degree of the technical difficulty. To reach this goal, a free style test of 8x100 meters and the Borg scale was used (to study the perceptions of the effort and the fatigue), as well as a question about the degree of the technical difficulty. No differences were found with respect to the perception of the effort and the fatigue but Asperger swimmers show more trouble to assess the degree of the technical difficulty, even when all the exercises have the same difficulty. So, it seems hard to find an objective value to determine the degree of correlation of its answers with the actual difficulty.Ferrer-Contreras, MC.; Granero-Gallegos, A.; Ferrer Contreras, M. (2019). Percepción subjetiva de esfuerzo, fatiga y grado de dificultad técnica en nadadores con y sin Síndrome de Asperger. Revista Andaluza de Medicina del Deporte. 12(4):400-403. https://doi.org/10.33155/j.ramd.2019.01.009S400403124Arruza J, Alzate R., Valencia J. Esfuerzo percibido y frecuencia cardiaca: el control de la intensidad de los esfuerzos en el entrenamiento de judo. Rev Psicol Deport. 1996;5(2):29-40.Cuadrado-Reyes J, Chirosa LJ, Chirosa, IJ, Martín-Tamayo I, Aguilar-Martínez D. La percepción subjetiva del esfuerzo para el control de la carga de entrenamiento en una temporada en un equipo de balonmano. Rev Psicol Deport. 2012;21(2):331-9.Mallo J, Navarro E. Physical load imposed on soccer players during small-sided training games. J Sports Med Phys Fitness. 2008;48(2):166-71.Guner R, Kunduracioglu B, Ulkar B. Running velocities and heart rates at fixed blood lactate concentrations in young soccer players. Adv Ther. 2006;23(3):395-03.Gómez-Díaz AJ, Bradley PS, Díaz A, Pallarés JG. Percepción subjetiva del esfuerzo en fútbol profesional: relevancia de los indicadores físicos y psicológicos en el entrenamiento y la competición. Anal Psicol. 2013;29(3):656-61.Sánchez-López MC, Navarro-Mateu F, Castillo MD, Menárguez JF, Sánchez-Sánchez JA. Atención sanitaria basada en la evidencia: su aplicación a la práctica clínica. Consejería de Sanidad de la Región de Murcia, Murcia. 2007.Borg G. Psychophysical bases of perceived exertion. Med Sci Sports Exerc. 1982;14:337-81.Laurent CM, Green JM, Bishop PA, Sjökvist J, Schumacker RE, Richardson, MT et al. A practical approach to monitoring recovery: development of a perceived recovery status scale. J Strength Cond Res. 2011;25(3):620-8.Watt B, Grove R. Perceived exertion. Antecedents and applications. Sports Med. 1993;15(4):225-41.Arruza J. Estado de ánimo, esfuerzo percibido y frecuencia cardiaca. Un estudio aplicado al judo. FEEFD. 1996;3(2).Held T, Marti B. Substantial influence of level of endurance capacity on the association of perceived exertion with blood lactate accumulation. Int J Sports Med. 1999;20(1):34-9.Castañer M, Saüch G, Camerino O, Sánchez-Algarra P, Anguera MT. Percepción de la intensidad al esfuerzo: Un estudio multi-method en actividad física. CPD. 2015,15(1):83-8.Borg G. Physical performance and perceived exertion. Copenhagen: Lund Berlingska Boktryckeriet. 1962.Beniscelli V, Torregrosa M. Componentes del esfuerzo percibido en el fútbol de iniciación. CPD. 2010;10(1),7-21.Hernández-Álvarez JL, del Campo-Vecino J, Martínez-de-Haro V, Moya-Morales M. Percepción de esfuerzo en Educación Física y su relación con las directrices sobre actividad física. Rev Int Med Cienc Act Fís Deporte. 2010;10(40),609-19

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

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

GPU Implementation of multichannel adaptive algorithms for local active noise control

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMultichannel active noise control (ANC) systems are commonly based on adaptive signal processing algorithms that require high computational capacity, which constrains their practical implementation. Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform for multichannel scenarios. However, efficient use of parallel computation in the adaptive filtering context is not straightforward due to the feedback loops. This paper compares two GPU implementations of a multichannel feedforward local ANC system working as a real-time prototype. Both GPU implementations are based on the filtered-x Least Mean Square algorithms; one is based on the conventional filtered-x scheme and the other is based on the modified filtered-x scheme. Details regarding the parallelization of the algorithms are given. Finally, experimental results are presented to compare the performance of both multichannel ANC GPU implementations. The results show the usefulness of many-core devices for developing versatile, scalable, and low-cost multichannel ANC systems.This work was supported by the European Union ERDF and Spanish Government under Project TEC2012-38142-C04, and Generalitat Valenciana under Project PROMETEO/2009/013. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Thushara D. Abhayapala.Lorente Giner, J.; Ferrer Contreras, M.; Diego Antón, MD.; Gonzalez, A. (2014). GPU Implementation of multichannel adaptive algorithms for local active noise control. IEEE Transactions on Audio, Speech and Language Processing. 22(11):1624-1635. https://doi.org/10.1109/TASLP.2014.2344852S16241635221

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

An affine projection algorithm with variable step-size and projection order

It is known that the performance of adaptive algorithms is constrained by their computational cost. Thus, affine projection adaptive algorithms achieve higher convergence speed when the projection order increases, which is at the expense of a higher computational cost. However, regardless of computational cost, a high projection order also leads to higher final error at steady state. For this reason it seems advisable to reduce the computational cost of the algorithm when high convergence speed is not needed (steady state) and to maintain or increase this cost only when the algorithm is in transient state to encourage rapid transit to the permanent regime. The adaptive order affine projection algorithm presented here addresses this subject. This algorithm adapts its projection order and step size depending on its convergence state by simple and meaningful rules. Thus it achieves good convergence behavior at every convergence state and very low computational cost at steady state.This work was partially funded by Spanish MICINN TEC2009-13741, GV-PROMETEO/2009/0013, GV/2010/027 and UPV/2009-1034.Gonzalez, A.; Ferrer Contreras, M.; Diego Antón, MD.; Piñero Sipán, MG. (2012). An affine projection algorithm with variable step-size and projection order. Digital Signal Processing. 22(4):586-592. doi:10.1016/j.dsp.2012.03.004S58659222

Convex combination filtered-X algorithms for active noise control systems

Adaptive filtering schemes exhibit a compromise between convergence speed and steady-state mean square error. Trying to overcome this trade-off, convex combination of adaptive filters have been recently developed for system identification achieving better performance than traditional approaches. The purpose of this work is to apply the convex combination strategy to single-channel and multichannel active noise control systems. In these systems it is necessary to take into account the secondary path between the adaptive filter output and the error sensor and the possible unavailability of the disturbance signal, which depends on the filtering scheme considered. Even though this strategy involves a higher computational burden than the classic adaptive filters, it exhibits a good performance in terms of convergence speed and steady-state mean square error.This work was supported in part by the Spanish Ministerio de Ciencia e Innovacion TEC2009-13741, Generalitat Valenciana PROMETEO 2009/0013, Generalitat Valenciana GV/2010/027, and Universitat Politecnica de Valencia PAID-06-09. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Boaz Rafaely.Ferrer Contreras, M.; Gonzalez, A.; Diego Antón, MD.; Piñero Sipán, MG. (2013). Convex combination filtered-X algorithms for active noise control systems. IEEE Transactions on Audio, Speech and Language Processing. 21(1):154-165. https://doi.org/10.1109/TASL.2012.2215595S15416521

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Room equalization has become essential for sound reproduction systems to provide the listener with the desired acoustical sensation. Recently, adaptive filters have been proposed as an effective tool in the core of these systems. In this context, this paper introduces different novel schemes based on the combination of adaptive filters idea: a versatile and flexible approach that permits obtaining adaptive schemes combining the capabilities of several independent adaptive filters. In this way, we have investigated the advantages of a scheme called combination of block-based adaptive filters which allows a blockwise combination splitting the adaptive filters into nonoverlapping blocks. This idea was previously applied to the plant identification problem, but has to be properly modified to obtain a suitable behavior in the equalization application. Moreover, we propose a scheme with the aim of further improving the equalization performance using the a priori knowledge of the energy distribution of the optimal inverse filter, where the block filters are chosen to fit with the coefficients energy distribution. Furthermore, the biased block-based filter is also introduced as a particular case of the combination scheme, especially suited for low signal-to-noise ratios (SNRs) or sparse scenarios. Although the combined schemes can be employed with any kind of adaptive filter, we employ the filtered-x improved proportionate normalized least mean square algorithm as basis of the proposed algorithms, allowing to introduce a novel combination scheme based on partitioned block schemes where different blocks of the adaptive filter use different parameter settings. Several experiments are included to evaluate the proposed algorithms in terms of convergence speed and steady-state behavior for different degrees of sparseness and SNRs.The work of L. A. Azpicueta-Ruiz was supported in part by the Comtmidad de Madrid through CASI-CAM-CM under Grant S2013/ICE-2845, in part by the Spanish Ministry of Economy and Competitiveness through DAMA under Grant TIN2015-70308-REDT, and Grant TEC2014-52289-R, and in part by the European Union. The work of L. Fuster, M. Ferrer, and M. de Diego was supported in part by EU together with the Spanish Government under Grant TEC2015-67387-C4-1-R (MINECO/FEDER), and in part by the Cieneralitat Valenciana under Grant PROMETEOII/2014/003. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Simon Dodo.Fuster Criado, L.; Diego Antón, MD.; Azpicueta-Ruiz, LA.; Ferrer Contreras, M. (2016). Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes. IEEE/ACM Transactions on Audio, Speech and Language Processing. 24(10):1732-1745. https://doi.org/10.1109/TASLP.2016.2583065S17321745241

Multi-tone Active Noise Equalizer with Spatially Distributed User-selected Profiles

[EN] In this work we propose a multi-channel active noise equalizer (ANE) that can deal with multi-frequency noise signals and assigns simultaneously different equalization gains to each frequency component at each monitoring sensor. For this purpose, we state a pseudo-error noise signal for each sensor of the ANE, which has to be cancelled out in order to get the desired equalization profiles. Firstly the optimal analytic solution for the ANE filters in the case of single-frequency noise is provided, and an adaptive algorithm based on the Least Mean Squared (LMS) is proposed for the same case. We also show that this adaptive strategy reaches the theoretical solution in steady state. Secondly, we state an equivalent approach for the case of multi-frequency noise based on two alternatives: a common pseudo-error signal at each sensor for all frequencies, and a different pseudo-error signal at each sensor for each frequency. The analytic and adaptive solutions for the ANE control filters have been developed for both pseudo-error alternatives. Finally, the ability of the proposed ANE to achieve simultaneously different user-selected noise profiles in different locations has been validated by their transfer functions and simulations.This work was supported by EU jointly with Spanish Government and Generalitat Valenciana under Grants RTI2018-098085-BC41, PID2021-125736OB-I00 (MCIU/AEI/FEDER), RE D2018-102668-T, and PROMETEO/2019/109.Ferrer Contreras, M.; Diego Antón, MD.; Hassani, A.; Moonen, M.; Piñero, G.; Gonzalez, A. (2022). Multi-tone Active Noise Equalizer with Spatially Distributed User-selected Profiles. IEEE/ACM Transactions on Audio Speech and Language Processing. 30:3199-3213. https://doi.org/10.1109/TASLP.2022.3212833319932133