A Hybrid Global Minimization Scheme for Accurate Source Localization in Sensor Networks

We consider the localization problem of multiple wideband sources in a multi-path environment by coherently taking into account the attenuation characteristics and the time delays in the reception of the signal. Our proposed method leaves the space for unavailability of an accurate signal attenuation model in the environment by considering the model as an unknown function with reasonable prior assumptions about its functional space. Such approach is capable of enhancing the localization performance compared to only utilizing the signal attenuation information or the time delays. In this paper, the localization problem is modeled as a cost function in terms of the source locations, attenuation model parameters and the multi-path parameters. To globally perform the minimization, we propose a hybrid algorithm combining the differential evolution algorithm with the Levenberg-Marquardt algorithm. Besides the proposed combination of optimization schemes, supporting the technical details such as closed forms of cost function sensitivity matrices are provided. Finally, the validity of the proposed method is examined in several localization scenarios, taking into account the noise in the environment, the multi-path phenomenon and considering the sensors not being synchronized

Acoustic sensor network geometry calibration and applications

In the modern world, we are increasingly surrounded by computation devices with communication links and one or more microphones. Such devices are, for example, smartphones, tablets, laptops or hearing aids. These devices can work together as nodes in an acoustic sensor network (ASN). Such networks are a growing platform that opens the possibility for many practical applications. ASN based speech enhancement, source localization, and event detection can be applied for teleconferencing, camera control, automation, or assisted living. For this kind of applications, the awareness of auditory objects and their spatial positioning are key properties. In order to provide these two kinds of information, novel methods have been developed in this thesis. Information on the type of auditory objects is provided by a novel real-time sound classification method. Information on the position of human speakers is provided by a novel localization and tracking method. In order to localize with respect to the ASN, the relative arrangement of the sensor nodes has to be known. Therefore, different novel geometry calibration methods were developed. Sound classification The first method addresses the task of identification of auditory objects. A novel application of the bag-of-features (BoF) paradigm on acoustic event classification and detection was introduced. It can be used for event and speech detection as well as for speaker identification. The use of both mel frequency cepstral coefficient (MFCC) and Gammatone frequency cepstral coefficient (GFCC) features improves the classification accuracy. By using soft quantization and introducing supervised training for the BoF model, superior accuracy is achieved. The method generalizes well from limited training data. It is working online and can be computed in a fraction of real-time. By a dedicated training strategy based on a hierarchy of stationarity, the detection of speech in mixtures with noise was realized. This makes the method robust against severe noises levels corrupting the speech signal. Thus it is possible to provide control information to a beamformer in order to realize blind speech enhancement. A reliable improvement is achieved in the presence of one or more stationary noise sources. Speaker localization The localization method enables each node to determine the direction of arrival (DoA) of concurrent sound sources. The author's neuro-biologically inspired speaker localization method for microphone arrays was refined for the use in ASNs. By implementing a dedicated cochlear and midbrain model, it is robust against the reverberation found in indoor rooms. In order to better model the unknown number of concurrent speakers, an application of the EM algorithm that realizes probabilistic clustering according to auditory scene analysis (ASA) principles was introduced. Based on this approach, a system for Euclidean tracking in ASNs was designed. Each node applies the node wise localization method and shares probabilistic DoA estimates together with an estimate of the spectral distribution with the network. As this information is relatively sparse, it can be transmitted with low bandwidth. The system is robust against jitter and transmission errors. The information from all nodes is integrated according to spectral similarity to correctly associate concurrent speakers. By incorporating the intersection angle in the triangulation, the precision of the Euclidean localization is improved. Tracks of concurrent speakers are computed over time, as is shown with recordings in a reverberant room. Geometry calibration The central task of geometry calibration has been solved with special focus on sensor nodes equipped with multiple microphones. Novel methods were developed for different scenarios. An audio-visual method was introduced for the calibration of ASNs in video conferencing scenarios. The DoAs estimates are fused with visual speaker tracking in order to provide sensor positions in a common coordinate system. A novel acoustic calibration method determines the relative positioning of the nodes from ambient sounds alone. Unlike previous methods that only infer the positioning of distributed microphones, the DoA is incorporated and thus it becomes possible to calibrate the orientation of the nodes with a high accuracy. This is very important for all applications using the spatial information, as the triangulation error increases dramatically with bad orientation estimates. As speech events can be used, the calibration becomes possible without the requirement of playing dedicated calibration sounds. Based on this, an online method employing a genetic algorithm with incremental measurements was introduced. By using the robust speech localization method, the calibration is computed in parallel to the tracking. The online method is be able to calibrate ASNs in real time, as is shown with recordings of natural speakers in a reverberant room. The informed acoustic sensor network All new methods are important building blocks for the use of ASNs. The online methods for localization and calibration both make use of the neuro-biologically inspired processing in the nodes which leads to state-of-the-art results, even in reverberant enclosures. The high robustness and reliability can be improved even more by including the event detection method in order to exclude non-speech events. When all methods are combined, both semantic information on what is happening in the acoustic scene as well as spatial information on the positioning of the speakers and sensor nodes is automatically acquired in real time. This realizes truly informed audio processing in ASNs. Practical applicability is shown by application to recordings in reverberant rooms. The contribution of this thesis is thus not only to advance the state-of-the-art in automatically acquiring information on the acoustic scene, but also pushing the practical applicability of such methods

Classification and localization of electromagnetic and ultrasonic pulsed emitters

Mención Internacional en el título de doctorThe localization of radiative sources is very important in many fields of work such as: sonar, radar and underwater radar, indoor localization in wireless networks, earthquake epicenter localization, defective assets localization in electrical facilities and so forth. In the process of locating radiative sources exist many issues which can provoke errors in the localization. The signals acquired may belong to different sources or they can be mixed with environmental noise, then, their separation before using localization algorithms is of great interest to be efficient and accurate in the computational process. Furthermore, the geometry and radiation characteristics of the receivers, the nature of the signal or their measuring process may cause deviations in the signal onset calculus and therefore the source localization could be displaced from the actual position. In this thesis, there are three kinds of algorithms to undertake three steps in the emitter localization: signal separation, onset and time delay estimation of the signals and source localization. For each step, in order to reduce the error in the localization, several algorithms are analyzed and compared in each application, to choose the most reliable. As the first step, to separate different kinds of signals is of interest to facilitate further processing. In this thesis, different optimization techniques are presented over the power ratio (PR) maps method. The PR uses a selective spectral signal characterization to extract the features of the analyzed signals. The technique identifies automatically the most representative frequency bands which report a great separation of the different kinds of signals in the PR map. After separating and selecting the signals, it is of interest to compare the algorithms to calculate the onset and time delay of the pulsed signals to know their performance because the time variables are inputs to the most common triangulation algorithms to locate radiative and ultrasonic sources. An overview of the algorithms used to estimate the time of flight (ToF) and time differences of arrival (TDoA) of pulsed signals is done in this thesis. In the comparison, there is also a new algorithm based on statics of high order, which is proposed in this thesis. The survey of their performance is done applied to muscle deep estimation, localization in one dimension (1D), and for the localization of emitters in three dimensions (3D). The results show how the presented algorithm yields great results. As the last step in the radiative source localization, the formulation and principle of work of both iterative and non-iterative triangulation algorithms are presented. A new algorithm is presented as a combination of two already existing improving their performance when working alone. All the algorithms, the proposed and the previous which already exist, are compared in terms of accuracy and computational time. The proposed algorithm reports good results in terms of accuracy and it is one of the fastest in computational time. Once the localization is achieved, it is of great interest to understand how the errors in the determination of the onset of the signals are propagated in the emitter localization. The triangulation algorithms estimate the radiative source position using time variables as inputs: ToF, TDoA or pseudo time of flight (pToF) and the receiver positions. The propagation of the errors in the time variables to the radiative source localization is done in two dimensions (2D) and 3D. New spherical diagrams have been created to represent the directions where the localization is more or less sensible to the errors. This study and their sphere diagrams are presented for several antenna layouts. Finally, how the errors in the positioning of the receivers are propagated to the emitter localization is analyzed. In this study, the effect in the propagation of both the relative distance from the receivers to the emitter and the direction between them has been characterized. The propagation of the error considering the direction is also represented in spherical diagrams. For a preferred direction identified in the spheres, the propagated error in the source localization has been quantified regarding both the source distance and the magnitude of the errors in the receivers positioning.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Andrea Cavallini.- Secretario: José Antonio García Souto.- Vocal: Iliana Portugués Peter

Mathematical modelling ano optimization strategies for acoustic source localization in reverberant environments

La presente Tesis se centra en el uso de técnicas modernas de optimización y de procesamiento de audio para la localización precisa y robusta de personas dentro de un entorno reverberante dotado con agrupaciones (arrays) de micrófonos. En esta tesis se han estudiado diversos aspectos de la localización sonora, incluyendo el modelado, la algoritmia, así como el calibrado previo que permite usar los algoritmos de localización incluso cuando la geometría de los sensores (micrófonos) es desconocida a priori. Las técnicas existentes hasta ahora requerían de un número elevado de micrófonos para obtener una alta precisión en la localización. Sin embargo, durante esta tesis se ha desarrollado un nuevo método que permite una mejora de más del 30\% en la precisión de la localización con un número reducido de micrófonos. La reducción en el número de micrófonos es importante ya que se traduce directamente en una disminución drástica del coste y en un aumento de la versatilidad del sistema final. Adicionalmente, se ha realizado un estudio exhaustivo de los fenómenos que afectan al sistema de adquisición y procesado de la señal, con el objetivo de mejorar el modelo propuesto anteriormente. Dicho estudio profundiza en el conocimiento y modelado del filtrado PHAT (ampliamente utilizado en localización acústica) y de los aspectos que lo hacen especialmente adecuado para localización. Fruto del anterior estudio, y en colaboración con investigadores del instituto IDIAP (Suiza), se ha desarrollado un sistema de auto-calibración de las posiciones de los micrófonos a partir del ruido difuso presente en una sala en silencio. Esta aportación relacionada con los métodos previos basados en la coherencia. Sin embargo es capaz de reducir el ruido atendiendo a parámetros físicos previamente conocidos (distancia máxima entre los micrófonos). Gracias a ello se consigue una mejor precisión utilizando un menor tiempo de cómputo. El conocimiento de los efectos del filtro PHAT ha permitido crear un nuevo modelo que permite la representación 'sparse' del típico escenario de localización. Este tipo de representación se ha demostrado ser muy conveniente para localización, permitiendo un enfoque sencillo del caso en el que existen múltiples fuentes simultáneas. La última aportación de esta tesis, es el de la caracterización de las Matrices TDOA (Time difference of arrival -Diferencia de tiempos de llegada, en castellano-). Este tipo de matrices son especialmente útiles en audio pero no están limitadas a él. Además, este estudio transciende a la localización con sonido ya que propone métodos de reducción de ruido de las medias TDOA basados en una representación matricial 'low-rank', siendo útil, además de en localización, en técnicas tales como el beamforming o el autocalibrado

Positioning aquatic animals with acoustic transmitters

Geolocating aquatic animals with acoustic tags has been ongoing for decades, relying on the detection of acoustic signals at multiple receivers with known positions to calculate a 2D or 3D position, and ultimately recreate the path of an aquatic animal from detections at fixed stations.This method of underwater geolocation is evolving with new software and hardware options available to help investigators design studies and calculate positions using solvers based predominantly on time-difference-of-arrival and time-of-arrival.We provide an overview of the considerations necessary to implement positioning in aquatic acoustic telemetry studies, including how to design arrays of receivers, test performance, synchronize receiver clocks and calculate positions from the detection data. We additionally present some common positioning algorithms, including both the free open-source solvers and the 'black-box' methods provided by some manufacturers for calculating positions.This paper is the first to provide a comprehensive overview of methods and considerations for designing and implementing better positioning studies that will support users, and encourage further knowledge advances in aquatic systems

Acoustic Source Localisation in constrained environments

Acoustic Source Localisation (ASL) is a problem with real-world applications across multiple domains, from smart assistants to acoustic detection and tracking. And yet, despite the level of attention in recent years, a technique for rapid and robust ASL remains elusive – not least in the constrained environments in which such techniques are most likely to be deployed. In this work, we seek to address some of these current limitations by presenting improvements to the ASL method for three commonly encountered constraints: the number and configuration of sensors; the limited signal sampling potentially available; and the nature and volume of training data required to accurately estimate Direction of Arrival (DOA) when deploying a particular supervised machine learning technique. In regard to the number and configuration of sensors, we find that accuracy can be maintained at state-of-the-art levels, Steered Response Power (SRP), while reducing computation sixfold, based on direct optimisation of well known ASL formulations. Moreover, we find that the circular microphone configuration is the least desirable as it yields the highest localisation error. In regard to signal sampling, we demonstrate that the computer vision inspired algorithm presented in this work, which extracts selected keypoints from the signal spectrogram, and uses them to select signal samples, outperforms an audio fingerprinting baseline while maintaining a compression ratio of 40:1. In regard to the training data employed in machine learning ASL techniques, we show that the use of music training data yields an improvement of 19% against a noise data baseline while maintaining accuracy using only 25% of the training data, while training with speech as opposed to noise improves DOA estimation by an average of 17%, outperforming the Generalised Cross-Correlation technique by 125% in scenarios in which the test and training acoustic environments are matched.Heriot-Watt University James Watt Scholarship (JSW) in the School of Engineering & Physical Sciences

Vocal Behaviour of the Eastern Indian Ocean Pygmy Blue Whale and Its Changes over Time and Between Aggregation Areas

This thesis investigates the calling behaviour of the eastern Indian Ocean pygmy blue (EIOPB) using long term data collected from passive acoustic recorders in the Perth Canyon, Western Australia (31.917° S, 115.031° E) and Portland, Victoria South Eastern Australia (38.536° S, 141.242° E). The research contained within this thesis highlights and quantifies the variability in song production of the eastern Indian Ocean pygmy blue whale, highlighting the capacity for blue whales to exhibit behavioural plasticity and building upon the understanding of singing behaviour in baleen whales

Acoustic source localisation and tracking using microphone arrays

This thesis considers the domain of acoustic source localisation and tracking in an indoor environment. Acoustic tracking has applications in security, human-computer interaction, and the diarisation of meetings. Source localisation and tracking is typically a computationally expensive task, making it hard to process on-line, especially as the number of speakers to track increases. Much of the literature considers single-source localisation, however a practical system must be able to cope with multiple speakers, possibly active simultaneously, without knowing beforehand how many speakers are present. Techniques are explored for reducing the computational requirements of an acoustic localisation system. Techniques to localise and track multiple active sources are also explored, and developed to be more computationally efficient than the current state of the art algorithms, whilst being able to track more speakers. The first contribution is the modification of a recent single-speaker source localisation technique, which improves the localisation speed. This is achieved by formalising the implicit assumption by the modified algorithm that speaker height is uniformly distributed on the vertical axis. Estimating height information effectively reduces the search space where speakers have previously been detected, but who may have moved over the horizontal-plane, and are unlikely to have significantly changed height. This is developed to allow multiple non-simultaneously active sources to be located. This is applicable when the system is given information from a secondary source such as a set of cameras allowing the efficient identification of active speakers rather than just the locations of people in the environment. The next contribution of the thesis is the application of a particle swarm technique to significantly further decrease the computational cost of localising a single source in an indoor environment, compared the state of the art. Several variants of the particle swarm technique are explored, including novel variants designed specifically for localising acoustic sources. Each method is characterised in terms of its computational complexity as well as the average localisation error. The techniques’ responses to acoustic noise are also considered, and they are found to be robust. A further contribution is made by using multi-optima swarm techniques to localise multiple simultaneously active sources. This makes use of techniques which extend the single-source particle swarm techniques to finding multiple optima of the acoustic objective function. Several techniques are investigated and their performance in terms of localisation accuracy and computational complexity is characterised. Consideration is also given to how these metrics change when an increasing number of active speakers are to be localised. Finally, the application of the multi-optima localisation methods as an input to a multi-target tracking system is presented. Tracking multiple speakers is a more complex task than tracking single acoustic source, as observations of audio activity must be associated in some way with distinct speakers. The tracker used is known to be a relatively efficient technique, and the nature of the multi-optima output format is modified to allow the application of this technique to the task of speaker tracking

Passive acoustic localization of sperm whales to facilitate ship strike avoidance

Ship strikes are one of the leading causes of premature mortality among whales, accounting for the deaths of approximately 20,000 each year, with untold more being injured. Given the exponential increase in shipping traffic, estimated at 2 - 3% year-over-year, the potential for collisions continues to grow. Due to their large size, preferred habitats and sea surface behavior, the sperm whale is one of the species most vulnerable to ship strikes. In some populations, collisions with maritime vessels are the leading cause of death, premature or otherwise. This is particularly concerning considering that the sperm whale is listed on the IUCN Red List of Threatened Species as “Vulnerable” globally and “Endangered” in the Mediterranean region. Passive Acoustic Monitoring, or PAM, is an environmentally non-intrusive method by which naturally generated underwater sounds, such as the clicks made by sperm whales, are picked up by hydrophones (underwater recording devices) and analyzed to extract a variety of data, including the sound source’s location. In the current research, we use a PAM methodology known as Time Difference of Arrival (TDOA) analysis, whereby different acoustic paths taken by sound waves from their source to a hydrophone are analyzed to extract the differences in time between their arrivals. Extracted TDOAs are compared to a theoretical model (in our case, the Bellhop ray tracing model) to extrapolate the source’s localization, which can then be fed into a live marine traffic system such as MarineTraffic (marinetraffic.com) to alert ships in the area to the presence and locations of the whales, so that they may take preventative action. In this dissertation, I present, inter alia, a working prototype, developed on the Matlab platform, for the detection and localization of sperm whales based on their vocalizations (clicks).As colisões com navios são uma das principais causas de mortalidade prematura entre as baleias, sendo responsáveis pela morte de aproximadamente 20.000 baleias a cada ano, com um número incontável de feridos. Os números exatos são difíceis de determinar porque as baleias feridas frequentemente saem para o mar, onde morrem e depois afundam no fundo do mar, não sendo mais avistadas. Em casos raros, baleias feridas podem encalhar, como aconteceu na costa de Almada, Portugal, em abril de 2022, quando um cachalote com lacerações visíveis – acredita-se ter sido causado por emaranhamento nos rotores de um navio – deu à costa e posteriormente morreu. Se há algum valor redentor para esses encalhes, é que eles fornecem aos cientistas uma pletora de informações inestimáveis sobre as causas e efeitos das colisões com navios. O cachalote é, sem dúvida, a espécie de cetáceo mais afetada por colisões com embarcações marítimas. Num estudo de 2018 de Díaz-Delgado et al., exames patológicos de 224 cetáceos encalhados, compreendendo 21 espécies diferentes, revelaram que o cachalote é, de longe, a espécie mais afetada por colisões de navios, representando quase metade (11 de 24) de todos os animais necropsiados mortos dessa maneira. Em algumas populações de cachalotes, as colisões com embarcações marítimas são tão comuns que são a principal causa de morte, prematura ou não. É o caso da região das Ilhas Canárias, onde se estima que cerca de 60% das mortes de cachalotes sejam atribuídas a golpes de navios. Infelizmente, as colisões de navios com os cachalotes continuam a aumentar a uma taxa exponencial. Existem três razões identificáveis para isso: A primeira é um aumento exponencial correspondente no tráfego marítimo. O número total de navios mercantes em todo o mundo mais que duplicou nos 8 anos entre 2004 e 2012 e mais que triplicou entre 1992 e 2012. Em 2018, mais de 92.000 navios mercantes navegaram pelos mares, transportando cerca de 12 mil milhões de toneladas de carga, um aumento de 35% em apenas uma década. O segundo fator é o comportamento específico de forrageamento do cachalote. Os cachalotes passam até 22 horas por dia caçando presas, incluindo cefalópodes e raias do fundo do mar, em profundidades de, em média, 600 a 1.000 metros, mas foram observados a mais de 2.000 metros. Nessas profundidades, eles devem enfrentar pressões extremas (superiores a 100 atmosferas) e escuridão total durante suas expedições de forrageamento, que podem durar até uma hora de cada vez. Como a visão é inútil nessas condições, acredita-se que os cachalotes usem a ecolocalização para localizar suas presas, produzindo uma série de cliques extremamente potentes que podem atingir níveis sonoros de 230 dB re 1 μPa a 1 m, tornando-os o som de maior intensidade gerado por qualquer animal existente. Quando a baleia emerge (o que, como mamífero dotado de pulmões e não de guelras, eventualmente deve fazê-lo), está exausta de caçar, suster a respiração, suportar pressões extremas e gerar cliques intensos, e deve descansar na superfície do mar, geralmente por cerca de 10 minutos, antes de iniciar seu próximo mergulho. É durante esses períodos de descanso que os cachalotes ficam relativamente imóveis e, portanto, particularmente vulneráveis a colisões com navios. Por uma infeliz coincidência, em certas áreas do mundo, como as Ilhas Canárias e ao largo da costa sudoeste da península do Peloponeso e Creta, é diretamente nas rotas marítimas movimentadas que os cachalotes escolhem descansar, porque as características batimétricas que atraem cachalotes por suas excelentes oportunidades de forrageamento, como as beiras das plataformas continentais e desfiladeiros íngremes, se sobrepõem às principais rotas de navegação nessas regiões. Um terceiro fator que causa um aumento no número de choques entre navios e cetáceos é o aumento do ruído submarino antropogénico que, como o volume de transporte, cresceu exponencialmente e que tem um efeito profundo não apenas nos cachalotes, mas na fauna marinha em geral. Estima-se que entre 1950 e 2007, os níveis de ruído ambiente de baixa frequência (25 - 50 Hz) nos oceanos, causados principalmente pelo constante ronco de fundo dos motores dos navios, aumentaram a uma taxa de 3,3 dB por década, chegando a 91 dB re 1 μPa2/Hz em 2007, a mesma intensidade de um assobio de golfinho. Como os decibéis são calculados em escala logarítmica, isso equivale a duplicar a intensidade do ruído a cada 10 anos. Os efeitos do aumento da poluição sonora sobre cachalotes e outros cetáceos são múltiplos, incluindo permanente deficiência auditiva, dessensibilização comportamental aos perigos associados ao ruído do navio e interrupções na disponibilidade e distribuição de suas presas. Embora a questão das colisões entre navios e baleias tenha recebido atenção crescente nos últimos anos, muito mais precisa de ser feito para mitigar esta forma completamente desnecessária e trágica de mortalidade de cetáceos. No caso dos cachalotes, há urgência, uma vez que muitos populações de cachalotes nunca se recuperaram tão rapidamente quanto o esperado após a promulgação da moratória internacional sobre a baleação comercial em 1986. Hoje em dia, a população mundial de cachalotes gira em torno de 200.000, pouco mais do que nos anos imediatamente anteriores à moratória, e muito menos do que os estimados 2 a 3 milhões de cachalotes que percorriam os mares em 1700, pouco antes de a espécie ser alvo de caça intensiva no século 19 por seu espermacete, uma mistura de ésteres de cera e triglicerídeos secretados nos órgãos produtores de som do crânio que era altamente valioso para uso em perfumes e velas. Como resultado, o cachalote permanece na Lista Vermelha de Espécies Ameaçadas da IUCN como "Vulnerável" globalmente e “Em Perigo, com Tendência Populacional: Diminuindo” na região do Mediterrâneo A Monitorização Acústica Passiva, ou PAM, é um método pelo qual sons subaquáticos gerados naturalmente, como cliques de cachalotes, são captados por hidrofones (dispositivos de gravação subaquática) e analisados para extrair uma variedade de dados. A PAM está a tornar-se cada vez mais popular na monitorização de cetáceos em geral, e cachalotes em particular, devido à combinação única de propriedades acústicas do clique “usual” do cachalote – volume, impulsividade e ampla faixa de frequência – o que o torna particularmente adequado para uso em técnicas de PAM. A vantagem da PAM em relação à monitorização da fauna marinha é que ele não é intrusivo: os sistemas PAM não geram som próprio e não envolvem contato direto com os animais. No entanto, a PAM pode ser usada efetivamente em uma variedade de aplicações, incluindo a determinação da presença e da localização de fontes de vocalização. Na investigação atual, usamos um método conhecido como análise de diferença de tempo de chegada (TDOA), em que diferentes caminhos percorridos pelas ondas sonoras à medida que viajam entre a fonte e o hidrofone, como caminhos diretos e os refletidos na superfície, são analisados para extrair a diferença de tempo entre os tempos de chegada. Os TDOAs extraídos são então comparados a um modelo teórico (no nosso caso, o modelo de traçamento de raios Bellhop), para extrapolar informações de localização sobre a fonte, incluindo a sua profundidade, distância horizontal (alcance) do hidrofone e ângulo de azimute. O resultado final pretendido é a localização quase em tempo real de todos os cachalotes nas proximidades de uma série de hidrofones, que podem então ser incluídos num sistema de tráfego marítimo em tempo real, como o MarineTraffic (marinetraffic.com) para alertar os navios na área para a presença e a localização das baleias, para que possam tomar ações preventivas, como reencaminhamento, redução de velocidade e colocação de observadores humanos de mamíferos marinhos no convés. Nesta dissertação, apresento, entre outros, um protótipo de trabalho, desenvolvido na plataforma Matlab, para a deteção e localização de cachalotes com base nas suas vocalizações (cliques)