Inverse problems related to physical processes are of great importance in practically every field related to signal processing, such as tomography, acoustics, wireless communications, medical and radar imaging, to name only a few. At the same time, many of these problems are quite challenging due to their ill-posed nature. On the other hand, signals originating from physical phenomena are often governed by laws expressible through linear Partial Differential Equations (PDE), or equivalently, integral equations and the associated Green’s functions. In addition, these phenomena are usually induced by sparse singularities, appearing as sources or sinks of a vector field. In this thesis we primarily investigate the coupling of such physical laws with a prior assumption on the sparse origin of a physical process. This gives rise to a “dual” regularization concept, formulated either as sparse analysis (cosparse), yielded by a PDE ...

Reverberation is the sum of reflected sound waves and is present in any conventional room. Speech communication devices such as mobile phones in hands-free mode, tablets, smart TVs, teleconferencing systems, hearing aids, voice-controlled systems, etc. use one or more microphones to pick up the desired speech signals. When the microphones are not in the proximity of the desired source, strong reverberation and noise can degrade the signal quality at the microphones and can impair the intelligibility and the performance of automatic speech recognizers. Therefore, it is a highly demanded task to process the microphone signals such that reverberation and noise are reduced. The process of reducing or removing reverberation from recorded signals is called dereverberation. As dereverberation is usually a completely blind problem, where the only available information are the microphone signals, and as the acoustic scenario can be non-stationary, ...

Room acoustic signal enhancement (RASE) applications, such as digital equalization, acoustic echo and feedback cancellation, which are commonly found in communication devices and audio equipment, aim at processing the acoustic signals with the final goal of improving the perceived sound quality in rooms. In order to do so, signal processing algorithms require the acoustic response of the room to be represented by means of parametric models and to be identified from the input and output signals of the room acoustic system. In particular, a good model should be both accurate, thus capturing those features of room acoustics that are physically and perceptually most relevant, and efficient, so that it can be implemented as a digital filter and used in practical signal processing tasks. This thesis addresses the fundamental question in room acoustic signal processing concerning the appropriateness of different parametric ...

This thesis investigates the design and evaluation of an embedded optimization framework for the perceptual enhancement of audio signals which are degraded by linear and/or nonlinear distortion. In general, audio signal enhancement has the goal to improve the perceived audio quality, speech intelligibility, or another desired perceptual attribute of the distorted audio signal by applying a real-time digital signal processing algorithm. In the designed embedded optimization framework, the audio signal enhancement problem under consideration is formulated and solved as a per-frame numerical optimization problem, allowing to compute the enhanced audio signal frame that is optimal according to a desired perceptual attribute. The first stage of the embedded optimization framework consists in the formulation of the per-frame optimization problem aimed at maximally enhancing the desired perceptual attribute, by explicitly incorporating a suitable model of human sound perception. The second stage of ...

In today's audio production and reproduction as well as in music performance practices it has become common practice to alter reverberation artificially through electronics or electro-acoustics. For music productions, radio plays, and movie soundtracks, the sound is often captured in small studio spaces with little to no reverberation to save real estate and to ensure a controlled environment such that the artistically intended spatial impression can be added during post-production. Spatial sound reproduction systems require flexible adjustment of artificial reverberation to the diffuse sound portion to help the reconstruction of the spatial impression. Many modern performance spaces are multi-purpose, and the reverberation needs to be adjustable to the desired performance style. Employing electro-acoustic feedback, also known as Reverberation Enhancement Systems (RESs), it is possible to extend the physical to the desired reverberation. These examples demonstrate a wide range of applications ...

Determining the geometry of an acoustic enclosure using microphone arrays has become an active area of research. Knowledge gained about the acoustic environment, such as the location of reflectors, can be advantageous for applications such as sound source localization, dereverberation and adaptive echo cancellation by assisting in tracking environment changes and helping the initialization of such algorithms. A methodology to blindly infer the geometry of an acoustic enclosure by estimating the location of reflective surfaces based on acoustic measurements using an arbitrary array geometry is developed and analyzed. The starting point of this work considers a geometric constraint, valid both in two and three-dimensions, that converts time-of-arrival and time-difference-of-arrival information into elliptical constraints about the location of reflectors. Multiple constraints are combined to yield the line or plane parameters of the reflectors by minimizing a specific cost function in the ...

In many speech communication applications, such as hands-free telephony and hearing aids, the microphones are located at a distance from the speaker. Therefore, in addition to the desired speech signal, the microphone signals typically contain undesired reverberation and noise, caused by acoustic reflections and undesired sound sources. Since these disturbances tend to degrade the quality of speech communication, decrease speech intelligibility and negatively affect speech recognition, efficient dereverberation and denoising methods are required. This thesis deals with blind dereverberation methods, not requiring any knowledge about the room impulse responses between the speaker and the microphones. More specifically, we propose a general framework for blind speech dereverberation based on multi-channel linear prediction (MCLP) and exploiting sparsity of the speech signal in the time-frequency domain.

Sound source localization algorithms have a long research history in the field of digital signal processing. Many common applications like intelligent personal assistants, teleconferencing systems and methods for technical diagnosis in acoustics require an accurate localization of sound sources in the environment. However, dynamic environments entail a particular challenge for these systems. For instance, voice controlled smart home applications, where the speaker, as well as potential noise sources, are moving within the room, are a typical example of dynamic environments. Classical sound source localization systems only have limited capabilities to deal with dynamic acoustic scenarios. In this thesis, three novel approaches to sound source localization that extend existing classical methods will be presented. The first system is proposed in the context of audiovisual source localization. Determining the position of sound sources in adverse acoustic conditions can be improved by including ...

Localization, separation and tracking of acoustic sources are ancient challenges that lots of animals and human beings are doing intuitively and sometimes with an impressive accuracy. Artificial methods have been developed for various applications and conditions. The majority of those methods are centralized, meaning that all signals are processed together to produce the estimation results. The concept of distributed sensor networks is becoming more realistic as technology advances in the fields of nano-technology, micro electro-mechanic systems (MEMS) and communication. A distributed sensor network comprises scattered nodes which are autonomous, self-powered modules consisting of sensors, actuators and communication capabilities. A variety of layout and connectivity graphs are usually used. Distributed sensor networks have a broad range of applications, which can be categorized in ecology, military, environment monitoring, medical, security and surveillance. In this dissertation we develop algorithms for distributed sensor networks ...

In many hands-free speech communication applications such as teleconferencing or voice-controlled applications, the recorded microphone signals do not only contain the desired speech signal, but also attenuated and delayed copies of the desired speech signal due to reverberation as well as additive background noise. Reverberation and background noise cause a signal degradation which can impair speech intelligibility and decrease the performance for many signal processing techniques. Acoustic multi-channel equalization techniques, which aim at inverting or reshaping the measured or estimated room impulse responses between the speech source and the microphone array, comprise an attractive approach to speech dereverberation since in theory perfect dereverberation can be achieved. However in practice, such techniques suffer from several drawbacks, such as uncontrolled perceptual effects, sensitivity to perturbations in the measured or estimated room impulse responses, and background noise amplification. The aim of this thesis ...

Due to significant advances made over the last few decades in the areas of (wireless) networking, communications and microprocessor fabrication, the use of sensor networks to observe physical phenomena is rapidly becoming commonplace. Over this period, many aspects of sensor networks have been explored, yet a thorough understanding of how to analyse and process the vast amounts of sensor data collected remains an open area of research. This work, therefore, aims to provide theoretical, as well as practical, advances this area. In particular, we consider the problem of inferring certain underlying properties of the monitored phenomena, from our sensor measurements. Within mathematics, this is commonly formulated as an inverse problem; whereas in signal processing, it appears as a (multidimensional) sampling and reconstruction problem. Indeed it is well known that inverse problems are notoriously ill-posed and very demanding to solve; meanwhile ...

In typical speech communication applications, such as hands-free mobile telephony, voice-controlled systems and hearing aids, the recorded microphone signals are corrupted by background noise, room reverberation and far-end echo signals. This signal degradation can lead to total unintelligibility of the speech signal and decreases the performance of automatic speech recognition systems. In this thesis several multi-microphone noise reduction and dereverberation techniques are developed. In Part I we present a Generalised Singular Value Decomposition (GSVD) based optimal filtering technique for enhancing multi-microphone speech signals which are degraded by additive coloured noise. Several techniques are presented for reducing the computational complexity and we show that the GSVD-based optimal filtering technique can be integrated into a `Generalised Sidelobe Canceller' type structure. Simulations show that the GSVD-based optimal filtering technique achieves a larger signal-to-noise ratio improvement than standard fixed and adaptive beamforming techniques and ...

This work is focused on the processing of multichannel and multisource audio signals. From an audio mixture of several audio sources recorded in a reverberant room, we wish to es- timate the acoustic responses (a.k.a. mixing filters) between the sources and the microphones. To solve this inverse problem one need to take into account additional hypotheses on the nature of the acoustic responses. Our approach consists in first identifying mathematically the neces- sary hypotheses on the acoustic responses for their estimation and then building cost functions and algorithms to effectively estimate them. First, we considered the case where the source signals are known. We developed a method to estimate the acoustic responses based on a convex regularization which exploits both the temporal sparsity of the filters and the exponentially decaying envelope. Real-world experi- ments confirmed the effectiveness of this method ...

Accurate auralisation remains the Holy Grail of room acoustics. Until now the models used for room impulse response (RIR) simulation have been either impractical to use due to excessive computational loading or based upon simplified approaches, unable to provide the levels of perceptual accuracy required by many applications. An example is the archaeological acoustic investigation of the intriguing properties of Neolithic passage graves such as Newgrange. After reviewing the currently available options, this thesis concentrates on digital waveguide mesh (DWM) physical modelling, on the premise that the three-dimensional (3D) version of this technique can be developed to provide the desired accuracy with reasonable computation times. Various 3D-mesh topologies, namely rectilinear, tetrahedral, octahedral and cubic close-packed (CCP), are analysed. Room simulation packages have been implemented for the rectilinear and tetrahedral topologies. Both are capable of generating highly scalable parallel models through ...

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 ...