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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.

Microphone windscreens are typically used to reduce the noise associated with wind flowing over a microphone diaphragm by reducing the velocity of the airflow. While most windscreens are effective at reducing this noise, they do not protect the microphone from many natural elements, such as moisture, sand, and other small particles. The focus of this research was to design a windscreen that protects an array of five microphones located around a 4.5-inch diameter cylinder from these natural elements. The design goals were to have a wind noise attenuation of at least 8 dB, an insertion loss of less than 1 dB from 5-1000 Hz, and a phase shift error of less than 3% over the same range. Computer simulations and experimental testing were used to select two basic designs. Four experimental tests consisting of wind noise attenuation, sand entrapment, insertion loss, and phase change measurements were used to optimize the geometry of these designs. The wind noise attenuation was tested by spinning the microphone array on a long boom and by setting the array in front of a fan. Sand was blown at the windscreen in order to test how well the windscreen protects the microphone array from small particles in the velocity stream. The insertion loss of the windscreen was tested by comparing an incoming signal traveling through the windscreen to the same signal without the windscreen. Finally, the phase shift between microphones was measured using a single frequency and comparing the microphone measurements with and without the windscreen. These four tests were performed on two designs. The first design consists of two foam filled concentric cones set around the microphone array. The second design consists of tubes that project outward from each microphone diaphragm, and then curve downwards. Both final windscreen designs meet the desired requirements. They both reduce wind noise attenuation by approximately 9 dB in a 13 mph wind and over 16 dB in a 20 mph wind. They also have negligible insertion loss, have a phase shift error of less than 3%, and are very efficient at blocking particles from entering the windscreen.

The last few years have seen the start of a unique change in microphones for consumer devices such as smartphones or tablets. Almost all analogue capacitive microphones are being replaced by digital silicon microphones or MEMS microphones. MEMS microphones perform differently to conventional analogue microphones. Their greatest disadvantage is significantly increased self-noise or decreased SNR, while their most significant benefits are ease of design and manufacturing and improved sensitivity matching. This thesis presents research on speech processing, comparing conventional analogue microphones with the newly available digital MEMS microphones. Specifically, voice activity detection, speaker diarisation (who spoke when), speech separation and speech recognition are looked at in detail. In order to carry out this research different microphone arrays were built using digital MEMS microphones and corpora were recorded to test existing algorithms and devise new ones. Some corpora that were created for the purpose of this research will be released to the public in 2013. It was found that the most commonly used VAD algorithm in current state-of-theart diarisation systems is not the best-performing one, i.e. MLP-based voice activity detection consistently outperforms the more frequently used GMM-HMM-based VAD schemes. In addition, an algorithm was derived that can determine the number of active speakers in a meeting recording given audio data from a microphone array of known geometry, leading to improved diarisation results. Finally, speech separation experiments were carried out using different post-filtering algorithms, matching or exceeding current state-of-the art results. The performance of the algorithms and methods presented in this thesis was verified by comparing their output using speech recognition tools and simple MLLR adaptation and the results are presented as word error rates, an easily comprehensible scale. To summarise, using speech recognition and speech separation experiments, this thesis demonstrates that the significantly reduced SNR of the MEMS microphone can be compensated for with well established adaptation techniques such as MLLR. MEMS microphones do not affect voice activity detection and speaker diarisation performance.

Turbulent flow over a rough surface produces sound that radiates outside the near wall region. This noise source is often at a lower level than the noise created by edges and bluff body flows, but for applications with large surface area to perimeter ratios at low Mach number, this noise source can have considerable levels. In the first part of this dissertation, a detailed study is made of the ability of the Glegg & Devenport (2009) scattering theory to predict roughness noise. To this end, comparisons are made with measurements from cuboidal and hemispherical roughness with roughness Reynolds numbers, hu_Ï /ν, ranging from 24 to 197 and roughness height to boundary layer thickness ratios of 5 to 18. Their theory is shown to work very accurately to predict the noise from surfaces with large roughness Reynolds numbers, but for cases with highly inhomogeneous wall pressure fields, differences grow between estimation and measurement. For these surfaces, the absolute levels were underpredicted but the spectral shape of the measurement was correctly determined indicating that the relationship of the radiated noise with the wavenumber wall pressure spectrum and roughness geometry appears to remain relatively unchanged. In the second part of this dissertation, delay and sum beamforming and least-squares analyses were used to examine roughness noise recorded by a 36-sensor linear microphone array. These methods were employed to estimate the variation of source strengths through short fetches of large hemispherical and cuboidal element roughness. The analyses show that the lead rows of the fetches produced the greatest streamwise and spanwise noise radiation. The least-squares analysis confirmed the presence of streamwise and spanwise aligned dipoles emanating from each roughness element as suggested by the LES of Yang & Wang (2011). The least-squares calculated source strengths show that the streamwise aligned dipole is always stronger than that of the spanwise dipole, but the relative magnitude of the difference varies with frequency. / Ph. D.

In this thesis, a computationally efficient method for detecting a whistle and capturing it using a 4 microphone array is proposed. Furthermore, methods are developed to efficiently process the data captured from all the microphones to estimate the direction of the sound source. The accuracy, the shortcoming and the constraints of the method proposed are also discussed. There is an emphasis placed on being computationally efficient so that the methods may be implemented on a low cost microcontroller and be used to provide a heading to an Unmanned Ground Vehicle. / MS / As humans, we rely on our sense of hearing to help us interact with the outside world. It helps us to listen not just to other people but also for sounds that maybe a warning for us. It can often be the first warning we get of an impending danger as we might hear a predator before we see it or we might hear a car brake and slip before we turn to look at it. However, it is not merely the ability to hear a sound that makes hearing so useful. It is the fact that we can tell which direction the sound is coming from that makes it so important. That is what allows us to know which direction to turn towards to respond to someone or from which direction the sound warning us of danger is coming. We may not be able to pinpoint the location of the source with complete accuracy but we can discern the general heading. It was this idea that inspired this research work. We wanted to be capable of estimating where a sound is coming from while being computationally efficient so that it may be implemented in real time with the help of a low cost microcontroller. This would then be used to provide a heading to an Unmanned Ground Vehicle while keeping the costs down.

The concept of replacing threads with flexible wires and sensors in a fabric to provide an underlying platform for integrating electronic components is known as e-textiles. This concept can be used to design applications involving different types of electronic components including sensors, digital signal processors, microcontrollers, color-changing fibers, and power sources. The adaptability of the textiles to the needs of the individual and the functionality of electronics can be integrated to provide unobtrusive, robust, and inexpensive clothing with novel features. This thesis focuses on the design of e-textiles for acoustic signal processing applications. This research examines challenges encountered when developing e-textile applications involving distributed arrays of microphones. A framework for designing such applications is presented. The design process and the performance analysis of two e-textiles, a large-scale beamforming fabric and a speech-processing vest, are presented. / Master of Science

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<div>The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.</div>

Indiana University-Purdue University Indianapolis (IUPUI) / The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.