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 ...

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 ...

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.

Understanding speech in noise and the occurrence of acoustic feedback belong to the major problems of current hearing aid users. Hence, an urgent demand exists for efficient and well-working digital signal processing algorithms that offer a solution to these issues. In this thesis we develop adaptive filtering techniques for noise reduction and acoustic feedback cancellation. Thanks to the availability of low power digital signal processors, these algorithms can be integrated in a hearing aid. Because of the ongoing miniaturization in the hearing aid industry and the growing tendency towards multi-microphone hearing aids, robustness against imperfections such as microphone mismatch, has become a major issue in the design of a noise reduction algorithm. In this thesis we propose multimicrophone noise reduction techniques that are based on multi-channel Wiener filtering (MWF). Theoretical and experimental analysis demonstrate that these MWF-based techniques are less ...

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.

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 ...

In modern devices which provide hands-free speech capturing functionality, such as hands-free communication kits and voice-controlled devices, the received speech signal at the microphones is corrupted by background noise, interfering speech signals, and room reverberation. In many practical situations, the microphones are not necessarily located near the desired source, and hence, the ratio of the desired speech power to the power of the background noise, the interfering speech, and the reverberation at the microphones can be very low, often around or even below 0 dB. In such situations, the comfort of human-to-human communication, as well as the accuracy of automatic speech recognisers for voice-controlled applications can be signi cantly degraded. Therefore, e ffective speech enhancement algorithms are required to process the microphone signals before transmitting them to the far-end side for communication, or before feeding them into a speech recognition ...

For a number of applications like acoustic echo cancellation, adaptive filters are required to identify very long impulse responses. To reduce the computational cost in implementations, adaptive filtering in subband is known to be beneficial. Based on a review of popular fullband adaptive filtering algorithms and various subband approaches, this thesis investigates the implementation, design, and limitations of oversampled subband adaptive filter systems based on modulated complex and real valued filter banks. The main aim is to achieve a computationally efficient implementation for adaptive filter systems, for which fast methods of performing both the subband decomposition and the subband processing are researched. Therefore, a highly efficient polyphase implementation of a complex valued modulated generalized DFT (GDFT) lter bank with a judicious selection of properties for non-integer oversampling ratios is introduced. By modification, a real valued single sideband modulated lter bank ...

This thesis proposes different methods to reduce the computational complexity as well as increasing the adaptation rate of adaptive broadband beamformers. This is performed exemplarily for the generalised sidelobe canceller (GSC) structure. The GSC is an alternative implementation of the linearly constrained minimum variance beamformer, which can utilise well-known adaptive filtering algorithms, such as the least mean square (LMS) or the recursive least squares (RLS) to perform unconstrained adaptive optimisation. A direct DFT implementation, by which broadband signals are decomposed into frequency bins and processed by independent narrowband beamforming algorithms, is thought to be computationally optimum. However, this setup fail to converge to the time domain minimum mean square error (MMSE) if signal components are not aligned to frequency bins, resulting in a large worst case error. To mitigate this problem of the so-called independent frequency bin (IFB) processor, overlap-save ...

The telecommunications sector is characterized by an increasing demand for user-friendliness and interactivity. This explains the growing interest in hands-free communication systems. Signal quality in current hands-free systems is unsatisfactory. To overcome this, advanced signal processing techniques such as the subband and frequency-domain adaptive filter are employed to enhance the signal. These techniques are known to have computationally efficient solutions. Furthermore, thanks to the frequency-dependent processing and adaptivity, highly time-varying systems and signals with a continuously changing spectral content such as speech can be handled. This thesis deals with subband and frequency-domain adaptive filtering techniques for speech enhancement in hands-free communication. The text consists of four parts. In the first part design methods for perfect and nearly perfect reconstruction DFT modulated filter banks are discussed. Part II deals with subband and frequency-domain adaptive filtering. The subband adaptive filter and the ...

Recently emerging techniques like wave field synthesis (WFS) or Higher-Order Ambisonics (HOA) allow for high-quality spatial audio reproduction, which makes them candidates for the audio reproduction in future telepresence systems or interactive gaming environments with acoustic human-machine interfaces. In such scenarios, acoustic echo cancellation (AEC) will generally be necessary to remove the loudspeaker echoes in the recorded microphone signals before further processing. Moreover, the reproduction quality of WFS or HOA can be improved by adaptive pre-equalization of the loudspeaker signals, as facilitated by listening room equalization (LRE). However, AEC and LRE require adaptive filters, where the large number of reproduction channels of WFS and HOA imply major computational and algorithmic challenges for the implementation of adaptive filters. A technique called wave-domain adaptive filtering (WDAF) promises to master these challenges. However, known literature is still far away from providing sufficient insight ...

This thesis deals with the efficient and flexible acquisition and processing of spatial sound using multiple microphones. In spatial sound acquisition and processing, we use multiple microphones to capture the sound of multiple sources being simultaneously active at a rever- berant recording side and process the sound depending on the application at the application side. Typical applications include source extraction, immersive spatial sound reproduction, or speech enhancement. A flexible sound acquisition and processing means that we can capture the sound with almost arbitrary microphone configurations without constraining the application at the ap- plication side. This means that we can realize and adjust the different applications indepen- dently of the microphone configuration used at the recording side. For example in spatial sound reproduction, where we aim at reproducing the sound such that the listener perceives the same impression as if he ...

Never before has speech been captured as often by electronic devices equipped with one or multiple microphones, serving a variety of applications. It is the key aspect in digital telephony, hearing devices, and voice-driven human-to-machine interaction. When speech is recorded, the microphones also capture a variety of further, undesired sound components due to adverse acoustic conditions. Interfering speech, background noise and reverberation, i.e. the persistence of sound in a room after excitation caused by a multitude of reflections on the room enclosure, are detrimental to the quality and intelligibility of target speech as well as the performance of automatic speech recognition. Hence, speech enhancement aiming at estimating the early target-speech component, which contains the direct component and early reflections, is crucial to nearly all speech-related applications presently available. In this thesis, we compare, propose and evaluate existing and novel approaches ...

The advent of multi-microphone setups on a plethora of commercial devices in recent years has generated a newfound interest in the development of robust microphone array signal processing methods. These methods are generally used to either estimate parameters associated with acoustic scene or to extract signal(s) of interest. In most practical scenarios, the sources are located in the far-field of a microphone array where the main spatial information of interest is the direction-of-arrival (DOA) of the plane waves originating from the source positions. The focus of this thesis is to incorporate robustness against either lack of or imperfect/erroneous information regarding the DOAs of the sound sources within a microphone array signal processing framework. The DOAs of sound sources is by itself important information, however, it is most often used as a parameter for a subsequent processing method. One of 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 ...