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

Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks

Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor

Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks

Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor

Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling

We consider the problem of separating speech sources captured by multiple spatially separated devices, each of which has multiple microphones and samples its signals at a slightly different rate. Most asynchronous array processing methods rely on sample rate offset estimation and resampling, but these offsets can be difficult to estimate if the sources or microphones are moving. We propose a source separation method that does not require offset estimation or signal resampling. Instead, we divide the distributed array into several synchronous subarrays. All arrays are used jointly to estimate the time-varying signal statistics, and those statistics are used to design separate time-varying spatial filters in each array. We demonstrate the method for speech mixtures recorded on both stationary and moving microphone arrays.Comment: To appear at the International Workshop on Acoustic Signal Enhancement (IWAENC 2018

Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks

Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor

ChordMics: Acoustic Signal Purification with Distributed Microphones

Acoustic signal acts as an essential input to many systems. However, the pure acoustic signal is very difficult to extract, especially in noisy environments. Existing beamforming systems are able to extract the signal transmitted from certain directions. However, since microphones are centrally deployed, these systems have limited coverage and low spatial resolution. We overcome the above limitations and present ChordMics, a distributed beamforming system. By leveraging the spatial diversity of the distributed microphones, ChordMics is able to extract the acoustic signal from arbitrary points. To realize such a system, we further address the fundamental challenge in distributed beamforming: aligning the signals captured by distributed and unsynchronized microphones. We implement ChordMics and evaluate its performance under both LOS and NLOS scenarios. The evaluation results tell that ChordMics can deliver higher SINR than the centralized microphone array. The average performance gain is up to 15dB

SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field

Simultaneous ranging and self-positioning in unsynchronized wireless acoustic sensor networks

Automatic ranging and self-positioning is a very desirable property in wireless acoustic sensor networks (WASNs) where nodes have at least one microphone and one loudspeaker. However, due to environmental noise, interference and multipath effects, audio-based ranging is a challenging task. This paper presents a fast ranging and positioning strategy that makes use of the correlation properties of pseudo-noise (PN) sequences for estimating simultaneously relative time-of-arrivals (TOAs) from multiple acoustic nodes. To this end, a proper test signal design adapted to the acoustic node transducers is proposed. In addition, a novel self-interference reduction method and a peak matching algorithm are introduced, allowing for increased accuracy in indoor environments. Synchronization issues are removed by following a BeepBeep strategy, providing range estimates that are converted to absolute node positions by means of multidimensional scaling (MDS). The proposed approach is evaluated both with simulated and real experiments under different acoustical conditions. The results using a real network of smartphones and laptops confirm the validity of the proposed approach, reaching an average ranging accuracy below 1 centimeter.This work was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-70202-P, TEC2012-37945-C02-02 and FEDER funds