Array signal processing for source localization and enhancement

“A common approach to the wide-band microphone array problem is to assume a certain array geometry and then design optimal weights (often in subbands) to meet a set of desired criteria. In addition to weights, we consider the geometry of the microphone arrangement to be part of the optimization problem. Our approach is to use particle swarm optimization (PSO) to search for the optimal geometry while using an optimal weight design to design the weights for each particle’s geometry. The resulting directivity indices (DI’s) and white noise SNR gains (WNG’s) form the basis of the PSO’s fitness function. Another important consideration in the optimal weight design are several regularization parameters. By including those parameters in the particles, we optimize their values as well in the operation of the PSO. The proposed method allows the user great flexibility in specifying desired DI’s and WNG’s over frequency by virtue of the PSO fitness function. Although the above method discusses beam and nulls steering for fixed locations, in real time scenarios, it requires us to estimate the source positions to steer the beam position adaptively. We also investigate source localization of sound and RF sources using machine learning techniques. As for the RF source localization, we consider radio frequency identification (RFID) antenna tags. Using a planar RFID antenna array with beam steering capability and using received signal strength indicator (RSSI) value captured for each beam position, the position of each RFID antenna tag is estimated. The proposed approach is also shown to perform well under various challenging scenarios”--Abstract, page iv

Advanced algorithms for audio and image processing

The objective of the thesis is the development of a set of innovative algorithms around the topic of beamforming in the field of acoustic imaging, audio and image processing, aimed at significantly improving the performance of devices that exploit these computational approaches. Therefore the context is the improvement of devices (ultrasound machines and video/audio devices) already on the market or the development of new ones which, through the proposed studies, can be introduced on new the markets with the launch of innovative high-tech start-ups. This is the motivation and the leitmotiv behind the doctoral work carried out. In fact, in the first part of the work an innovative image reconstruction algorithm in the field of ultrasound biomedical imaging is presented, which is connected to the development of such equipment that exploits the computing opportunities currently offered nowadays at low cost by GPUs (Moore\u2019s law). The proposed target is to obtain a new pipeline of the reconstruction of the image abandoning the architecture of such hardware based In the first part of the thesis I faced the topic of the reconstruction of ultrasound images for applications hypothesized on a software based device through image reconstruction algorithms processed in the frequency domain. An innovative beamforming algorithm based on seismic migration is presented, in which a transformation of the RF data is carried out and the reconstruction algorithm can evaluate a masking of the k-space of the data, speeding up the reconstruction process and reducing the computational burden. The analysis and development of the algorithms responsible for carrying out the thesis has been approached from a feasibility point in an off-line context and on the Matlab platform, processing both synthetic simulated generated data and real RF data: the subsequent development of these algorithms within of the future ultrasound biomedical equipment will exploit an high-performance computing framework capable of processing customized kernel pipelines (henceforth called \u2019filters\u2019) on CPU/GPU. The type of filters implemented involved the topic of Plane Wave Imaging (PWI), an alternative method of acquiring the ultrasound image compared to the state of the art of the traditional standard B-mode which currently exploit sequential sequence of insonification of the sample under examination through focused beams transmitted by the probe channels. The PWI mode is interesting and opens up new scenarios compared to the usual signal acquisition and processing techniques, with the aim of making signal processing in general and image reconstruction in particular faster and more flexible, and increasing importantly the frame rate opens up and improves clinical applications. The innovative idea is to introduce in an offline seismic reconstruction algorithm for ultrasound imaging a further filter, named masking matrix. The masking matrices can be computed offline knowing the system parameters, since they do not depend from acquired data. Moreover, they can be pre-multiplied to propagation matrices, without affecting the overall computational load. Subsequently in the thesis, the topic of beamforming in audio processing on super-direct linear arrays of microphones is addressed. The aim is to make an in depth analysis of two main families of data-independent approaches and algorithms present in the literature by comparing their performances and the trade-off between directivity and frequency invariance, which is not yet known at to the state-of-the-art. The goal is to validate the best algorithm that allows, from the perspective of an implementation, to experimentally verify performance, correlating it with the characteristics and error statistics. Frequency-invariant beam patterns are often required by systems using an array of sensors to process broadband signals. In some experimental conditions, the array spatial aperture is shorter than the involved wavelengths. In these conditions, superdirective beamforming is essential for an efficient system. I present a comparison between two methods that deal with a data-independent beamformer based on a filter-and-sum structure. Both methods (the first one numerical, the second one analytic) formulate a mathematical convex minimization problem, in which the variables to be optimized are the filters coefficients or frequency responses. In the described simulations, I have chosen a geometry and a set-up of parameters that allows us to make a fair comparison between the performances of the two different design methods analyzed. In particular, I addressed a small linear array for audio capture with different purposes (hearing aids, audio surveillance system, video-conference system, multimedia device, etc.). The research activity carried out has been used for the launch of a high-tech device through an innovative start-up in the field of glasses/audio devices (https://acoesis.com/en/). It has been proven that the proposed algorithm gives the possibility of obtaining higher performances than the state of the art of similar algorithms, additionally providing the possibility of connecting directivity or better generalized directivity to the statistics of phase errors and gain of sensors, extremely important in superdirective arrays in the case of real and industrial implementation. Therefore, the method proposed by the comparison is innovative because it quantitatively links the physical construction characteristics of the array to measurable and experimentally verifiable quantities, making the real implementation process controllable. The third topic faced is the reconstruction of the Room Impluse Response (RIR) using audio processing blind methods. Given an unknown audio source, the estimation of time differences-of-arrivals (TDOAs) can be efficiently and robustly solved using blind channel identification and exploiting the cross-correlation identity (CCI). Prior blind works have improved the estimate of TDOAs by means of different algorithmic solutions and optimization strategies, while always sticking to the case N = 2 microphones. But what if we can obtain a direct improvement in performance by just increasing N? In the fourth Chapter I tried to investigate this direction, showing that, despite the arguable simplicity, this is capable of (sharply) improving upon state-of-the-art blind channel identification methods based on CCI, without modifying the computational pipeline. Inspired by our results, we seek to warm up the community and the practitioners by paving the way (with two concrete, yet preliminary, examples) towards joint approaches in which advances in the optimization are combined with an increased number of microphones, in order to achieve further improvements. Sound source localisation applications can be tackled by inferring the time-difference-of-arrivals (TDOAs) between a sound-emitting source and a set of microphones. Among the referred applications, one can surely list room-aware sound reproduction, room geometry\u2019s estimation, speech enhancement. Despite a broad spectrum of prior works estimate TDOAs from a known audio source, even when the signal emitted from the acoustic source is unknown, TDOAs can be inferred by comparing the signals received at two (or more) spatially separated microphones, using the notion of cross-corrlation identity (CCI). This is the key theoretical tool, not only, to make the ordering of microphones irrelevant during the acquisition stage, but also to solve the problem as blind channel identification, robustly and reliably inferring TDOAs from an unknown audio source. However, when dealing with natural environments, such \u201cmutual agreement\u201d between microphones can be tampered by a variety of audio ambiguities such as ambient noise. Furthermore, each observed signal may contain multiple distorted or delayed replicas of the emitting source due to reflections or generic boundary effects related to the (closed) environment. Thus, robustly estimating TDOAs is surely a challenging problem and CCI-based approaches cast it as single-input/multi-output blind channel identification. Such methods promote robustness in the estimate from the methodological standpoint: using either energy-based regularization, sparsity or positivity constraints, while also pre-conditioning the solution space. Last but not least, the Acoustic Imaging is an imaging modality that exploits the propagation of acoustic waves in a medium to recover the spatial distribution and intensity of sound sources in a given region. Well known and widespread acoustic imaging applications are, for example, sonar and ultrasound. There are active and passive imaging devices: in the context of this thesis I consider a passive imaging system called Dual Cam that does not emit any sound but acquires it from the environment. In an acoustic image each pixel corresponds to the sound intensity of the source, the whose position is described by a particular pair of angles and, in the case in which the beamformer can, as in our case, work in near-field, from a distance on which the system is focused. In the last part of this work I propose the use of a new modality characterized by a richer information content, namely acoustic images, for the sake of audio-visual scene understanding. Each pixel in such images is characterized by a spectral signature, associated to a specific direction in space and obtained by processing the audio signals coming from an array of microphones. By coupling such array with a video camera, we obtain spatio-temporal alignment of acoustic images and video frames. This constitutes a powerful source of self-supervision, which can be exploited in the learning pipeline we are proposing, without resorting to expensive data annotations. However, since 2D planar arrays are cumbersome and not as widespread as ordinary microphones, we propose that the richer information content of acoustic images can be distilled, through a self-supervised learning scheme, into more powerful audio and visual feature representations. The learnt feature representations can then be employed for downstream tasks such as classification and cross-modal retrieval, without the need of a microphone array. To prove that, we introduce a novel multimodal dataset consisting in RGB videos, raw audio signals and acoustic images, aligned in space and synchronized in time. Experimental results demonstrate the validity of our hypothesis and the effectiveness of the proposed pipeline, also when tested for tasks and datasets different from those used for training. Chapter 6 closes the thesis, presenting a development activity of a new Dual Cam POC to build-up from it a spin-off, assuming to apply for an innovation project for hi-tech start- ups (such as a SME instrument H2020) for a 50Keuro grant, following the idea of the technology transfer. A deep analysis of the reference market, technologies and commercial competitors, business model and the FTO of intellectual property is then conducted. Finally, following the latest technological trends (https://www.flir.eu/products/si124/) a new version of the device (planar audio array) with reduced dimensions and improved technical characteristics is simulated, simpler and easier to use than the current one, opening up new interesting possibilities of development not only technical and scientific but also in terms of business fallout

Akustinen monimikrofonikeilanmuodostus käytännöllisessä laitteessa

Signal quality and noiselessness is important for Automatic Speech Recognition and communications. These can be achieved with the help of microphone arrays and beamforming. Multiple microphones can be used to pick sound coming from one direction while attenuating other directions. In this thesis, three arrays and three beamformers are compared. The target is to see how different structures affect the output signal and if one of the arrays is superior to the others. The results show that the baffled array appears bigger than the reference array or the array on the top surface of a cylinder, but it adds complexity to the beamformers. For Automatic Speech Recognition, all three arrays performed the same, but there were clear differences between the beamforming algorithms. Thus the array to use depends on the use-case and especially on the industrial design of the device.Äänenlaatu ja kohinattomuus ovat tärkeitä automaattisen puheentunnistuksen ja puheviestinnän toimivuuden kannalta. Mikrofoniryhmiä ja keilanmuodostusta voidaan käyttää parantamaan signaalin laatua ja vähentämään kohinaa nauhoittamalla vain yhdestä suunnasta tulevaa ääntä. Tässä työssä vertaillaan kolmea mikrofoniryhmää ja kolmea keilanmuodostusalgoritmia. Tarkoituksena on selvittää miten erilaiset rakenteet, joihin mikrofoniryhmä on kiinnitettynä vaikuttaa nauhoitettuun signaaliin, ja jos jokin ryhmistä on merkittävästi parempi kuin muut. Tulokset osoittavat, että sylinterin ulkoreunalle kiinnitetty mikrofoniryhmä vaikuttaa akustisesti suuremmalta kuin referenssi mikrofoniryhmä tai sylinterin päälipinnalle kiinnitetty mikrofoniryhmä, mutta se lisää keilanmuodostusalgoritmin monimutkaisuutta. Automaattisessa puheentunnistuksessa kaikki kolme mikrofoniryhmää suoriutuivat yhtä hyvin, mutta keilanmuodostusalgoritmien suorituskyvyssä oli selkeät erot. Käytännössä siis mikrofoniryhmä kannattaa valita käyttötarkoitukseen ja etenkin lopullisen tuotteen teollisen muotoiluun sopivaksi

The Capture and Recreation of 3D Auditory Scenes

The main goal of this research is to develop the theory and implement practical tools (in both software and hardware) for the capture and recreation of 3D auditory scenes. Our research is expected to have applications in virtual reality, telepresence, film, music, video games, auditory user interfaces, and sound-based surveillance. The first part of our research is concerned with sound capture via a spherical microphone array. The advantage of this array is that it can be steered into any 3D directions digitally with the same beampattern. We develop design methodologies to achieve flexible microphone layouts, optimal beampattern approximation and robustness constraint. We also design novel hemispherical and circular microphone array layouts for more spatially constrained auditory scenes. Using the captured audio, we then propose a unified and simple approach for recreating them by exploring the reciprocity principle that is satisfied between the two processes. Our approach makes the system easy to build, and practical. Using this approach, we can capture the 3D sound field by a spherical microphone array and recreate it using a spherical loudspeaker array, and ensure that the recreated sound field matches the recorded field up to a high order of spherical harmonics. For some regular or semi-regular microphone layouts, we design an efficient parallel implementation of the multi-directional spherical beamformer by using the rotational symmetries of the beampattern and of the spherical microphone array. This can be implemented in either software or hardware and easily adapted for other regular or semi-regular layouts of microphones. In addition, we extend this approach for headphone-based system. Design examples and simulation results are presented to verify our algorithms. Prototypes are built and tested in real-world auditory scenes

Robust Personal Audio Geometry Optimization in the SVD-Based Modal Domain

© 2014 IEEE. Personal audio generates sound zones in a shared space to provide private and personalized listening experiences with minimized interference between consumers. Regularization has been commonly used to increase the robustness of such systems against potential perturbations in the sound reproduction. However, the performance is limited by the system geometry such as the number and location of the loudspeakers and controlled zones. This paper proposes a geometry optimization method to find the most geometrically robust approach for personal audio amongst all available candidate system placements. The proposed method aims to approach the most 'natural' sound reproduction so that the solo control of the listening zone coincidently accompanies the preferred quiet zone. Being formulated in the SVD-based modal domain, the method is demonstrated by applications in three typical personal audio optimizations, i.e., the acoustic contrast control, the pressure matching, and the planarity control. Simulation results show that the proposed method can obtain the system geometry with better avoidance of 'occlusion,' improved robustness to regularization, and improved broadband equalization

Acoustic contrast, planarity and robustness of sound zone methods using a circular loudspeaker array

Since the mid 1990s, acoustics research has been undertaken relating to the sound zone problem—using loudspeakers to deliver a region of high sound pressure while simultaneously creating an area where the sound is suppressed—in order to facilitate independent listening within the same acoustic enclosure. The published solutions to the sound zone problem are derived from areas such as wave field synthesis and beamforming. However, the properties of such methods differ and performance tends to be compared against similar approaches. In this study, the suitability of energy focusing, energy cancelation, and synthesis approaches for sound zone reproduction is investigated. Anechoic simulations based on two zones surrounded by a circular array show each of the methods to have a characteristic performance, quantified in terms of acoustic contrast, array control effort and target sound field planarity. Regularization is shown to have a significant effect on the array effort and achieved acoustic contrast, particularly when mismatched conditions are considered between calculation of the source weights and their application to the system