BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-based Acoustic Big Data

This paper presents a novel BigEAR big data framework that employs psychological audio processing chain (PAPC) to process smartphone-based acoustic big data collected when the user performs social conversations in naturalistic scenarios. The overarching goal of BigEAR is to identify moods of the wearer from various activities such as laughing, singing, crying, arguing, and sighing. These annotations are based on ground truth relevant for psychologists who intend to monitor/infer the social context of individuals coping with breast cancer. We pursued a case study on couples coping with breast cancer to know how the conversations affect emotional and social well being. In the state-of-the-art methods, psychologists and their team have to hear the audio recordings for making these inferences by subjective evaluations that not only are time-consuming and costly, but also demand manual data coding for thousands of audio files. The BigEAR framework automates the audio analysis. We computed the accuracy of BigEAR with respect to the ground truth obtained from a human rater. Our approach yielded overall average accuracy of 88.76% on real-world data from couples coping with breast cancer.Comment: 6 pages, 10 equations, 1 Table, 5 Figures, IEEE International Workshop on Big Data Analytics for Smart and Connected Health 2016, June 27, 2016, Washington DC, US

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Smart Sound Control in Acoustic Sensor Networks: a Perceptual Perspective

[ES] Los sistemas de audio han experimentado un gran desarrollo en los últimos años gracias al aumento de dispositivos con procesadores de alto rendimiento capaces de realizar un procesamiento cada vez más eficiente. Además, las comunicaciones inalámbricas permiten a los dispositivos de una red estar ubicados en diferentes lugares sin limitaciones físicas. La combinación de estas tecnologías ha dado lugar a la aparición de las redes de sensores acústicos (ASN). Una ASN está compuesta por nodos equipados con transductores de audio, como micrófonos o altavoces. En el caso de la monitorización acústica del campo, sólo es necesario incorporar sensores acústicos a los nodos ASN. Sin embargo, en el caso de las aplicaciones de control, los nodos deben interactuar con el campo acústico a través de altavoces. La ASN puede implementarse mediante dispositivos de bajo coste, como Raspberry Pi o dispositivos móviles, capaces de gestionar varios micrófonos y altavoces y de ofrecer una buena capacidad de cálculo. Además, estos dispositivos pueden comunicarse mediante conexiones inalámbricas, como Wi-Fi o Bluetooth. Por lo tanto, en esta tesis, se propone una ASN compuesta por dispositivos móviles conectados a altavoces inalámbricos mediante un enlace Bluetooth. Además, el problema de la sincronización entre los dispositivos de una ASN es uno de los principales retos a abordar, ya que el rendimiento del procesamiento de audio es muy sensible a la falta de sincronismo. Por lo tanto, también se lleva a cabo un análisis del problema de sincronización entre dispositivos conectados a altavoces inalámbricos en una ASN. En este sentido, una de las principales aportaciones es el análisis de la latencia de audio cuando los nodos acústicos de la ASN están formados por dispositivos móviles que se comunican altavoces mediante enlaces Bluetooth. Una segunda contribución significativa de esta tesis es la implementación de un método para sincronizar los diferentes dispositivos de una ASN, junto con un estudio de sus limitaciones. Por último, se ha introducido el método propuesto para implementar aplicaciones de zonas sonoras personales (PSZ). Por lo tanto, la implementación y el análisis del rendimiento de diferentes aplicaciones de audio sobre una ASN compuesta por dispositivos móviles y altavoces inalámbricos es también una contribución significativa en el área de las ASN. Cuando el entorno acústico afecta negativamente a la percepción de la señal de audio emitida por los altavoces de la ASN, se uti­lizan técnicas de ecualización para mejorar la percepción de la señal de audio. Para ello, en esta tesis se implementa un sistema de ecualización inteligente. Para ello, se emplean algoritmos psicoacústicos para implementar un procesamiento inteligente basado en el sis­tema auditivo humano capaz de adaptarse a los cambios del entorno. Por ello, otra contribución importante de esta tesis es el análisis del enmas­caramiento espectral entre dos sonidos complejos. Este análisis permitirá calcular el umbral de enmascaramiento de un sonido con más precisión que los métodos utilizados actualmente. Este método se utiliza para implementar una aplicación de ecualización perceptiva que pretende mejorar la percepción de la señal de audio en presencia de un ruido ambien­tal. Para ello, esta tesis propone dos algoritmos de ecualización diferentes: 1) la pre-ecualización de la señal de audio para que se perciba por encima del umbral de enmascaramiento del ruido ambiental y 2) diseñar un con­trol de ruido ambiental perceptivo en los sistemas de ecualización activa de ruido (ANE), para que el nivel de ruido ambiental percibido esté por debajo del umbral de enmascaramiento de la señal de audio. Por lo tanto, la ultima aportación de esta tesis es la implementación de una aplicación de ecualización perceptiva con los dos diferentes algorit­mos de ecualización embebidos y el análisis de su rendimiento a través del banco de pruebas realizado en el laboratorio GTAC-iTEAM.[CA] El sistemes de so han experimentat un gran desenvolupament en els últims anys gràcies a l'augment de dispositius amb processadors d'alt rendiment capaços de realitzar un processament d'àudio cada vegada més eficient. D'altra banda, l'expansió de les comunicacions inalàmbriques ha permès implementar xarxes en les quals els dispositius poden estar situats a difer­ents llocs sense limitacions físiques. La combinació d'aquestes tecnologies ha donat lloc a l'aparició de les xarxes de sensors acústics (ASN). Una ASN està composta per nodes equipats amb transductors d'àudio, com micr`ofons o altaveus. En el cas del monitoratge del camp acústic, només cal incorporar sensors acústics als nodes de l'ASN. No obstant això, en el cas de les aplicacions de control, els nodes han d'interactuar amb el camp acústic a través d'altaveus. Una ASN pot implementar-se mitjant¿cant dispositius de baix cost, com ara Raspberry Pi o dispositius mòbils, capaços de gestionar di­versos micròfons i altaveus i d'oferir una bona capacitat computacional. A més, aquests dispositius poden comunicar-se a través de connexions inalàmbriques, com Wi-Fi o Bluetooth. Per això, en aquesta tesi es proposa una ASN composta per dispositius mòbils connectats a altaveus inalàmbrics a través d'un enllaç Bluetooth. El problema de la sincronització entre els dispositius d'una ASN és un dels principals reptes a abordar ja que el rendiment del processament d'àudio és molt sensible a la falta de sincronisme. Per tant, també es duu a terme una anàlisi profunda del problema de la sincronització entre els dispositius comercials connectats als altaveus inalàmbrics en una ASN. En aquest sentit, una de les principals contribucions és l'anàlisi de la latència d'àudio quan els nodes acústics en l'ASN estan compostos per dispositius mòbils que es comuniquen amb els altaveus corresponents mitjançant enllaços Bluetooth. Una segona contribuciò sig­nificativa d'aquesta tesi és la implementació d'un mètode per sincronitzar els diferents dispositius d'una ASN, juntament amb un estudi de les seves limitacions. Finalment, s'ha introduït el mètode proposat per implemen­tar aplicacions de zones de so personal. Per tant, la implementació i l'anàlisi del rendiment de diferents aplicacions d'àudio sobre una ASN composta per dispositius mòbils i al­taveus inalàmbrics és també una contribució significativa a l'àrea de les ASN. Quan l'entorn acústic afecta negativament a la percepció del senyal d'àudio emesa pels altaveus de l'ASN, es fan servir tècniques d'equalització per a millorar la percepció del senyal d'àudio. En consequència, en aquesta tesi s'implementa un sistema d'equalització intel·ligent. Per això, s'utilitzen algoritmes psicoacústics per implementar un processament intel·ligent basat en el sistema audi­tiu humà capaç d'adaptar-se als canvis de l'entorn. Per aquest motiu, una altra contribució important d'aquesta tesi és l'anàlisi de l'emmascarament espectral entre dos sons complexos. Aquesta anàlisi permetrà calcular el llindar d'emmascarament d'un so sobre amb més precisió que els mètodes utilitzats actualment. Aquest mètode s'utilitza per a imple­mentar una aplicació d'equalització perceptual que pretén millorar la per­cepció del senyal d'àudio en presència d'un soroll ambiental. Per això, aquesta tesi proposa dos algoritmes d'equalització diferents: 1) la pree­qualització del senyal d'àudio perquè es percebi per damunt del llindar d'emmascarament del soroll ambiental i 2) dissenyar un control de soroll ambiental perceptiu en els sistemes d'equalització activa de soroll (ANE) de manera que el nivell de soroll ambiental percebut estiga per davall del llindar d'emmascarament del senyal d'àudio. Per tant, l'última aportació d'aquesta tesi és la implementació d'una aplicació d'equalització perceptiva amb els dos algoritmes d'equalització embeguts i l'anàlisi del seu rendiment a través del banc de proves realitzat al laboratori GTAC-iTEAM.[EN] Audio systems have been extensively developed in recent years thanks to the increase of devices with high-performance processors able to per­form more efficient processing. In addition, wireless communications allow devices in a network to be located in different places without physical limitations. The combination of these technologies has led to the emergence of Acoustic Sensor Networks (ASN). An ASN is com­posed of nodes equipped with audio transducers, such as microphones or speakers. In the case of acoustic field monitoring, only acoustic sensors need to be incorporated into the ASN nodes. However, in the case of control applications, the nodes must interact with the acoustic field through loudspeakers. ASN can be implemented through low-cost devices, such as Rasp­berry Pi or mobile devices, capable of managing multiple mi­crophones and loudspeakers and offering good computational capacity. In addition, these devices can communicate through wireless connections, such as Wi-Fi or Bluetooth. Therefore, in this dissertation, an ASN composed of mobile devices connected to wireless speak­ers through a Bluetooth link is proposed. Additionally, the problem of syn­chronization between the devices in an ASN is one of the main challenges to be addressed since the audio processing performance is very sensitive to the lack of synchronism. Therefore, an analysis of the synchroniza­tion problem between devices connected to wireless speakers in an ASN is also carried out. In this regard, one of the main contributions is the analysis of the audio latency of mobile devices when the acoustic nodes in the ASN are comprised of mobile devices communicating with the corresponding loudspeakers through Bluetooth links. A second significant contribution of this dissertation is the implementation of a method to synchronize the different devices of an ASN, together with a study of its limitations. Finally, the proposed method has been introduced in order to implement personal sound zones (PSZ) applications. Therefore, the imple­mentation and analysis of the performance of different audio applications over an ASN composed of mobile devices and wireless speakers is also a significant contribution in the area of ASN. In cases where the acoustic environment negatively affects the percep­tion of the audio signal emitted by the ASN loudspeakers, equalization techniques are used with the objective of enhancing the perception thresh­old of the audio signal. For this purpose, a smart equalization system is implemented in this dissertation. In this regard, psychoacous­tic algorithms are employed to implement a smart processing based on the human hearing system capable of adapting to changes in the envi­ronment. Therefore, another important contribution of this thesis focuses on the analysis of the spectral masking between two complex sounds. This analysis will allow to calculate the masking threshold of one sound over the other in a more accurate way than the currently used methods. This method is used to implement a perceptual equalization application that aims to improve the perception threshold of the audio signal in presence of ambient noise. To this end, this thesis proposes two different equalization algorithms: 1) pre-equalizing the audio signal so that it is perceived above the ambient noise masking threshold and 2) designing a perceptual control of ambient noise in active noise equalization (ANE) systems, so that the perceived ambient noise level is below the masking threshold of the audio signal. Therefore, the last contribution of this dissertation is the imple­mentation of a perceptual equalization application with the two different embedded equalization algorithms and the analysis of their performance through the testbed carried out in the GTAC-iTEAM laboratory.This work has received financial support of the following projects: • SSPRESING: Smart Sound Processing for the Digital Living (Reference: TEC2015-67387-C4-1-R. Entity: Ministerio de Economia y Empresa. Spain). • FPI: Ayudas para contratos predoctorales para la formación de doctores (Reference: BES-2016-077899. Entity: Agencia Estatal de Investigación. Spain). DANCE: Dynamic Acoustic Networks for Changing Environments (Reference: RTI2018-098085-B-C41-AR. Entity: Agencia Estatal de Investigación. Spain). • DNOISE: Distributed Network of Active Noise Equalizers for Multi-User Sound Control (Reference: H2020-FETOPEN-4-2016-2017. Entity: I+D Colaborativa competitiva. Comisión de las comunidades europea).Estreder Campos, J. (2022). Smart Sound Control in Acoustic Sensor Networks: a Perceptual Perspective [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181597TESI

Doctor of Philosophy

dissertationHearing aids suffer from the problem of acoustic feedback that limits the gain provided by hearing aids. Moreover, the output sound quality of hearing aids may be compromised in the presence of background acoustic noise. Digital hearing aids use advanced signal processing to reduce acoustic feedback and background noise to improve the output sound quality. However, it is known that the output sound quality of digital hearing aids deteriorates as the hearing aid gain is increased. Furthermore, popular subband or transform domain digital signal processing in modern hearing aids introduces analysis-synthesis delays in the forward path. Long forward-path delays are not desirable because the processed sound combines with the unprocessed sound that arrives at the cochlea through the vent and changes the sound quality. In this dissertation, we employ a variable, frequency-dependent gain function that is lower at frequencies of the incoming signal where the information is perceptually insignificant. In addition, the method of this dissertation automatically identifies and suppresses residual acoustical feedback components at frequencies that have the potential to drive the system to instability. The suppressed frequency components are monitored and the suppression is removed when such frequencies no longer pose a threat to drive the hearing aid system into instability. Together, the method of this dissertation provides more stable gain over traditional methods by reducing acoustical coupling between the microphone and the loudspeaker of a hearing aid. In addition, the method of this dissertation performs necessary hearing aid signal processing with low-delay characteristics. The central idea for the low-delay hearing aid signal processing is a spectral gain shaping method (SGSM) that employs parallel parametric equalization (EQ) filters. Parameters of the parametric EQ filters and associated gain values are selected using a least-squares approach to obtain the desired spectral response. Finally, the method of this dissertation switches to a least-squares adaptation scheme with linear complexity at the onset of howling. The method adapts to the altered feedback path quickly and allows the patient to not lose perceivable information. The complexity of the least-squares estimate is reduced by reformulating the least-squares estimate into a Toeplitz system and solving it with a direct Toeplitz solver. The increase in stable gain over traditional methods and the output sound quality were evaluated with psychoacoustic experiments on normal-hearing listeners with speech and music signals. The results indicate that the method of this dissertation provides 8 to 12 dB more hearing aid gain than feedback cancelers with traditional fixed gain functions. Furthermore, experimental results obtained with real world hearing aid gain profiles indicate that the method of this dissertation provides less distortion in the output sound quality than classical feedback cancelers, enabling the use of more comfortable style hearing aids for patients with moderate to profound hearing loss. Extensive MATLAB simulations and subjective evaluations of the results indicate that the method of this dissertation exhibits much smaller forward-path delays with superior howling suppression capability

System Identification, Diagnosis, and Built-In Self-Test of High Switching Frequency DC-DC Converters

abstract: Complex electronic systems include multiple power domains and drastically varying dynamic power consumption patterns, requiring the use of multiple power conversion and regulation units. High frequency switching converters have been gaining prominence in the DC-DC converter market due to smaller solution size (higher power density) and higher efficiency. As the filter components become smaller in value and size, they are unfortunately also subject to higher process variations and worse degradation profiles jeopardizing stable operation of the power supply. This dissertation presents techniques to track changes in the dynamic loop characteristics of the DC-DC converters without disturbing the normal mode of operation. A digital pseudo-noise (PN) based stimulus is used to excite the DC-DC system at various circuit nodes to calculate the corresponding closed-loop impulse response. The test signal energy is spread over a wide bandwidth and the signal analysis is achieved by correlating the PN input sequence with the disturbed output generated, thereby accumulating the desired behavior over time. A mixed-signal cross-correlation circuit is used to derive on-chip impulse responses, with smaller memory and lower computational requirement in comparison to a digital correlator approach. Model reference based parametric and non-parametric techniques are discussed to analyze the impulse response results in both time and frequency domain. The proposed techniques can extract open-loop phase margin and closed-loop unity-gain frequency within 5.2% and 4.1% error, respectively, for the load current range of 30-200mA. Converter parameters such as natural frequency (ω_n ), quality factor (Q), and center frequency (ω_c ) can be estimated within 3.6%, 4.7%, and 3.8% error respectively, over load inductance of 4.7-10.3µH, and filter capacitance of 200-400nF. A 5-MHz switching frequency, 5-8.125V input voltage range, voltage-mode controlled DC-DC buck converter is designed for the proposed built-in self-test (BIST) analysis. The converter output voltage range is 3.3-5V and the supported maximum load current is 450mA. The peak efficiency of the converter is 87.93%. The proposed converter is fabricated on a 0.6µm 6-layer-metal Silicon-On-Insulator (SOI) technology with a die area of 9mm^2 . The area impact due to the system identification blocks including related I/O structures is 3.8% and they consume 530µA quiescent current during operation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Who Said That? Towards a Machine-Prediction-Based Approach to Tursiops Truncatus Whistle Localization and Attribution in a Reverberant Dolphinarium

Dolphin communication research is an active period of growth. Many researchers expect to find significant communicative capacity in dolphins given their known sociality and large and complex brains. Moreover, given dolphins’ known acoustic sensitivity, serving their well-studied echolocation ability, some researchers have speculated that dolphin communication is mediated in large part by a sophisticated “vocal” language. However, evidence supporting this belief is scarce. Among most dolphin species, a particular tonal class of call, termed the whistle, has been identified as socially important. In particular, for the common bottlenose dolphin, Tursiops truncatus – arguably the focal species of most dolphin cognitive and communication research – research has fixated on “signature whistles,” individuallydistinctive whistles that seem to convey an individual’s identity to conspecifics, can be mimicked, and can be modulated under certain circumstances in ways that may or may not be communicative. Apart from signature whistles, most studies of dolphin calls concern group-based repertoires of whistles and other, pulse-form call types. However, studies of individual repertoires of non-signature whistles, and the phenomenon of combined signature and non-signature vocal exchanges among dolphins, are conspicuously rare in the literature, tending to be limited by either extreme subject confinement or sparse attributions of vocalizer identity. Nevertheless, such studies constitute a logical prerequisite to an understanding of the communicative potential of whistles. This absence can be explained by a methodological limitation in the way in which dolphin sounds are recorded. In particular, no established method exists for recording the whistles of an entire social group of dolphins so as to reliably attribute them to their vocalizers. This thesis proposes a dolphinarium-based system for achieving audio recording with whistle attribution, as well as visual behavioral tracking. Towards achieving the proposed system, I present foundational work involving the installation of permanent hydrophone arrays and cameras in a dolphinarium that enforces strict animal safety regulations. Attributing tonal sounds via the process of sound localization – estimation of a sound’s point of origin based on the physical properties of its propagation – in a highly reverberant environment is a notoriously difficult problem, resistant to many conventional signal processing techniques. This thesis will provide evidence of this difficulty, and also a demonstration of a highly e↵ective machine-learning-based solution to the problem. This thesis also provides miscellaneous hardware and the pieces of a computational pipeline towards completion of the full proposed, automated system. Once completed, the proposed system will provide an enormous data stream that will lend itself to large-scale studies of individual repertoires of non-signature whistles and combined signature and non-signature vocal exchanges among an invariant group of socializing dolphins, representing a unique and necessary achievement in dolphin communication research

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

Statistical signal processing for mechanical systems

Random processes such as temperature and acoustic noise are found in all types of mechanical systems. Knowledge of these processes can lead to improved design and detection methods related to faulty operation. The goal of this dissertation is to contribute to the knowledge base of such processes. Specifically, we address statistical signal processing methods that are appropriate and consistent relative to the physics of these systems. Two generic problems associated with random signal measurements from mechanical systems are addressed.;Random processes associated with mechanical systems usually have complex spectral structure containing both continuous and line spectral components. Accordingly, they are called mixed random processes. One problem addressed is to use variability related to families of spectral estimators for a mixed random process to better characterize its spectral information. We show that tones are a significant source of bias and variability of families of spectral estimators. Expressions for estimating statistical and arithmetic variability of three common families of spectral estimators are provided. An important and immediate application of these results is tone detection.;We also address the statistical problem of estimating the bandwidth parameter of a Gauss-Markov process from a realization of fixed and finite duration at selectable sampling interval. The motivation is that continuous-time processes are often sampled at a rate far higher than their underlying dynamics. It is commonly assumed a faster sample rate is better. But in many real world situations, such as in adaptive feedback control schemes design, short time changes demand only limited time being utilized. Thus this problem is investigated. The bias and variance expressions of the parameter estimator are derived with a second order expansion. Three sample rate regions---finite, large and very large ones, corresponding to substantial, gradual, and very slight variance drop, are quantitatively identified. Guidelines in choosing sampling rate based on estimator performance requirement are provided.;The results are used to characterize the stochastic structure of the sound pressure process from an engine cooling fan with and without mock engine, and to perform a hypothesis test for deciding whether a design change has a significant effect on the sound

ACTIVE NOISE CONTROL USING CARBON NANOTUBE THERMOPHONES: CASE STUDY FOR AN AUTOMOTIVE HVAC APPLICATION

The goal of this project was to reduce the overall noise levels emitted by the HVAC components in a vehicle’s cabin. More specifically, the feasibility of achieving this goal using two key technologies was investigated. The first of these technologies, Active Noise Control (ANC), is a noise attenuation technique that relies on destructive interference that “cancels” unwanted noise. Typically used in situations where physical constraints prevent passive attenuation techniques from being used, ANC is known for its high size-to-effectiveness ratio. This benefit cannot be gained without a cost however; the complexity of ANC systems is significantly higher than their passive counterparts. This is due to the signal processing and actuator designs required. These actuators often take the form of moving-coil loudspeakers which, while effective, are often bulky. Because of this they are difficult to “drop in” to an existing system. This is where the second technology comes in. Carbon Nanotube (CNT) Thermophones are solid-state speakers that operate by using rapid heat fluctuations to create sound. Called the “thermoacoustic effect,” (TE) the theory of this operating principle dates to the turn of the 20th century. Useful demonstration of TE did not occur until 2008, however, when researchers first developed the first CNT thermophones. The hallmark characteristics of these transducers are their small size and flexible nature. Compared to traditional loudspeakers they have a much smaller form factor and are more versatile in terms of where they can be placed in a cramped system. The marriage of CNT transducers to ANC technology shows promise in improving the application space and ease of installation of ANC systems. Getting these two to cooperate, however, is not without challenges. A case study for this union is presented here; the application space being the ducted environment of vehicle HVAC systems

An Optical Character Recognition Engine for Graphical Processing Units

This dissertation investigates how to build an optical character recognition engine (OCR) for a graphical processing unit (GPU). I introduce basic concepts for both building an OCR engine and for programming on the GPU. I then describe the SegRec algorithm in detail and discuss my findings