This thesis deals with several open problems in acoustic echo cancellation and acoustic feedback control. Our main goal has been to develop solutions that provide a high performance and sound quality, and behave in a robust way in realistic conditions. This can be achieved by departing from the traditional ad-hoc methods, and instead deriving theoretically well-founded solutions, based on results from parameter estimation and system identification. In the development of these solutions, the computational efficiency has permanently been taken into account as a design constraint, in that the complexity increase compared to the state-of-the-art solutions should not exceed 50 % of the original complexity. In the context of acoustic echo cancellation, we have investigated the problems of double-talk robustness, acoustic echo path undermodeling, and poor excitation. The two former problems have been tackled by including adaptive decorrelation filters in the ...

In the field of adaptive filtering the most commonly applied filter structure is the transversal filter, also referred to as the tapped-delay line (TDL). The TDL is composed of a cascade of unit delay elements that are tapped, weighted and then summed. Thus, the output of a TDL is formed by a linear combination of its input signal at various delays. The weights in this linear combination are called the tap weights. The number of delay elements, or equivalently the number of tap weights, determines the duration of the impulse response of the TDL. For this reason, one often speaks of a finite impulse response (FIR) filter. In a general adaptive filtering scheme the adaptive filter aims to minimize a certain measure of error between its output and a desired signal. Usually, a quadratic cost criterion is taken: the so-called ...

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 ...

Modern devices such as mobile phones, tablets or smart speakers are commonly equipped with several loudspeakers and microphones. If, for instance, one employs such a device for hands-free communication applications, the signals that are reproduced by the loudspeakers are propagated through the room and are inevitably acquired by the microphones. If no processing is applied, the participants in the far-end room receive delayed reverberated replicas of their own voice, which strongly degrades both speech intelligibility and user comfort. In order to prevent that so-called acoustic echoes are transmitted back to the far-end room, acoustic echo cancelers are commonly employed. The latter make use of adaptive filtering techniques to identify the propagation paths between loudspeakers and microphones. The estimated propagation paths are then employed to compute acoustic echo estimates, which are finally subtracted from the signals acquired by the microphones. In ...

Today, adaptive algorithms represent one of the most frequently used computational tools for the processing of digital speech signals. This work investigates and analyzes the properties of adaptive algorithms in speech communication applications where rigorous conditions apply, such as noise and echo cancelation. Like other theses in this field do, it tries to tackle the ever-lasting problem of computational complexity vs. rate of convergence. It introduces some new adaptive methods that stem from the existing algorithms as well as a novel concept which has been entitled Optimal Step-Size (OSS). In the first part of the thesis we investigate some well-known, widely used adaptive techniques such as the Normalized Least Mean Squares (NLMS) and the Recursive Least Mean Squares (RLS). In spite of the fact that the NLMS and the RLS belong to the "simplest" principles, as far as complexity is ...

Adaptive filtering algorithms are powerful signal processing tools with widespread use in numerous engineering applications. Computational complexity is a key factor in determining the optimal implementation as well as real-time performance of the adaptive signal processors. To minimize the required hardware and/or software resources for implementing an adaptive filtering algorithm, it is desirable to mitigate its computational complexity as much as possible without imposing any significant sacrifice of performance. This thesis comprises a collection of thirteen peer-reviewed published works as well as an integrating material. The works are along the lines of a common unifying theme that is to devise new low-complexity adaptive filtering algorithms for communications and, more generally, signal processing applications. The main contributions are the new adaptive filtering algorithms, channel equalization techniques, and theoretical analyses listed below under four categories: 1) adaptive system identification • affine projection ...

The analysis and design of new non-centralized learning algorithms for potential application in distributed adaptive estimation is the focus of this thesis. Such algorithms should be designed to have low processing requirement and to need minimal communication between the nodes which would form a distributed network. They ought, moreover, to have acceptable performance when the nodal input measurements are coloured and the environment is dynamic. Least mean square (LMS) and recursive least squares (RLS) type incremental distributed adaptive learning algorithms are first introduced on the basis of a Hamiltonian cycle through all of the nodes of a distributed network. These schemes require each node to communicate only with one of its neighbours during the learning process. An original steady-steady performance analysis of the incremental LMS algorithm is performed by exploiting a weighted spatial-temporal energy conservation formulation. This analysis confirms that ...

In this thesis, we develop a number of algorithms for acoustic echo and noise cancellation. We derive a fast exact implementation for the affine projection algorithm (APA), and we also show that when using strong regularization the existing (approximating) fast techniques exhibit problems. We develop a number of algorithms for noise cancellation based on optimal filtering techniques for multi-microphone systems. By using QR-decomposition based techniques, a complexity reduction of a factor 50 to 100 is achieved compared to existing implementations. Finally, we show that instead of using a cascade of a noise-cancellation system and an echo-cancellation system, it is better to solve the combined problem as a global optimization problem. The aforementioned noise reduction techniques can be used to solve this optimization problem.

Array processing is an area of study devoted to processing the signals received from an antenna array and extracting information of interest. It has played an important role in widespread applications like radar, sonar, and wireless communications. Numerous adaptive array processing algorithms have been reported in the literature in the last several decades. These algorithms, in a general view, exhibit a trade-off between performance and required computational complexity. In this thesis, we focus on the development of array processing algorithms in the application of beamforming and direction of arrival (DOA) estimation. In the beamformer design, we employ the constrained minimum variance (CMV) and the constrained constant modulus (CCM) criteria to propose full-rank and reduced-rank adaptive algorithms. Specifically, for the full-rank algorithms, we present two low-complexity adaptive step size mechanisms with the CCM criterion for the step size adaptation of the ...

This work considers a Broadcast Channel (BC) system, where the transmitter is equipped with multiple antennas and each user at the receiver side could have one or more antennas. Depending on the number of antennas at the receiver side, such a system is known as Multiple-User Multiple-Input Single-Output (MU-MISO), for single antenna users, or Multiple-UserMultiple-InputMultiple-Output (MU-MIMO), for several antenna users. This model is suitable for current wireless communication systems. Regarding the direction of the data flow, we differentiate between downlink channel or BC, and uplink channel or Multiple Access Channel (MAC). In the BC the signals are sent from the Base Station (BS) to the users, whereas the information from the users is sent to the BS in the MAC. In this work we focus on the BC where the BS applies linear precoding taking advantage of multiple antennas. The ...

Given the widespread use of miniaturized audio interfaces, echo control systems are faced with increasing challenges to address a large variety of acoustic conditions observed by such interfaces. This motivates the use of sophisticated machine learning-based techniques to overcome the limitations of conventional methods. The contributions in this thesis can be outlined by decomposing the task of nonlinear acoustic echo control into two subtasks: Nonlinear Acoustic Echo Cancellation (NAEC) and Acoustic Echo Suppression (AES). In particular, by formulating the single-channel NAEC model-adaptation task as a Bayesian recursive filtering problem, an evolutionary resampling strategy for particle filtering is proposed. The resulting Elitist Resampling Particle Filter (ERPF) is shown experimentally to be an efficient and high-performing approach that can be extended to address challenging conditions such as non-stationary interferers. The fundamental problem of nonlinear model design is addressed by proposing a novel ...

Multimedia consumer electronics are nowadays everywhere from teleconferencing, hands-free communications, in-car communications to smart TV applications and more. We are living in a world of telecommunication where ideal scenarios for implementing these applications are hard to find. Instead, practical implementations typically bring many problems associated to each real-life scenario. This thesis mainly focuses on two of these problems, namely, acoustic echo and acoustic feedback. On the one hand, acoustic echo cancellation (AEC) is widely used in mobile and hands-free telephony where the existence of echoes degrades the intelligibility and listening comfort. On the other hand, acoustic feedback limits the maximum amplification that can be applied in, e.g., in-car communications or in conferencing systems, before howling due to instability, appears. Even though AEC and acoustic feedback cancellation (AFC) are functional in many applications, there are still open issues. This means that ...

The ability to easily exchange and access data has transformed the way we work, study, inform and entertain ourselves. In particular, the Internet has had an effect on people’s lives in the past two decades that is profound. Profound as this effect may be, people seem not to grow tired of it. On the contrary: as of today, the Internet revolution is far from over. The thirst for bigger amounts of data at higher speeds and biquitous connectivity seem not to abate. This thirst for more, faster and better quality data is both a huge challenge and a huge opportunity for the broadband access industry. The opportunity lies on the fact that, as of the end of 2012, there were 600 million subscribers to broadband services around the world. Plus, even though the market is already enormous, it still has ...

In many distant speech acquisition scenarios, such as hands-free telephony or teleconferencing, the desired speech signal is corrupted by noise and reverberation. This degrades both the speech quality and intelligibility, making communication difficult or even impossible. Speech enhancement techniques seek to mitigate these effects and extract the desired speech signal. This objective is commonly achieved through the use of microphone arrays, which take advantage of the spatial properties of the sound field in order to reduce noise and reverberation. Spherical microphone arrays, where the microphones are arranged in a spherical configuration, usually mounted on a rigid baffle, are able to analyze the sound field in three dimensions; the captured sound field can then be efficiently described in the spherical harmonic domain (SHD). In this thesis, a number of novel spherical array processing algorithms are proposed, based in the SHD. In ...

When providing the necessary amplification in hearing aids, the risk of acoustic feedback is increased due to the coupling between the hearing aid loudspeaker and the hearing aid microphone(s). This acoustic feedback is often perceived as an annoying whistling or howling. Thus, to reduce the occurrence of acoustic feedback, robust and fast-acting feedback suppression algorithms are required. The main objective of this thesis is to develop and evaluate algorithms for robust and fast-acting feedback suppression in hearing aids. Specifically, we focus on enhancing the performance of adaptive filtering algorithms that estimate the feedback component in the hearing aid microphone by reducing the number of required adaptive filter coefficients and by improving the trade-off between fast convergence and good steady-state performance. Additionally, we develop fixed spatial filter design methods that can be applied in a multi-microphone earpiece.