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Über dieses Buch

This book treats the topic of extending the adaptive filtering theory in the context of massive multichannel systems by taking into account a priori knowledge of the underlying system or signal. The starting point is exploiting the sparseness in acoustic multichannel system in order to solve the non-uniqueness problem with an efficient algorithm for adaptive filtering that does not require any modification of the loudspeaker signals.
The book discusses in detail the derivation of general sparse representations of acoustic MIMO systems in signal or system dependent transform domains. Efficient adaptive filtering algorithms in the transform domains are presented and the relation between the signal- and the system-based sparse representations is emphasized. Furthermore, the book presents a novel approach to spatially preprocess the loudspeaker signals in a full-duplex communication system. The idea of the preprocessing is to prevent the echoes from being captured by the microphone array in order to support the AEC system. The preprocessing stage is given as an exemplarily application of a novel unified framework for the synthesis of sound figures. Finally, a multichannel system for the acoustic echo suppression is presented that can be used as a postprocessing stage for removing residual echoes. As first of its kind, it extracts the near-end signal from the microphone signal with a distortionless constraint and without requiring a double-talk detector.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

In research and development there is an increased interest in array-based audio signal processing. A major challenge to fully exploit the potential of array processing in practical applications lies in the development of adaptive MIMO systems. The underlying signal processing problems that are approached by adaptive systems can be classified as forward and inverse problems. In this chapter, the massive multichannel acoustic echo cancellation problem is introduced as an example of the forward problems. Also a brief review of the state-of-the-art in the multichannel adaptive filtering is given.
Karim Helwani

Theoretical Multichannel System Identification

Frontmatter

Chapter 2. Fundamentals of Adaptive Filter Theory

In this chapter we will treat some fundamentals of the adaptive filtering theory highlighting the system identification problem. We will introduce a signal and system model that will be used throughout this book.
Karim Helwani

Chapter 3. Spatio-Temporal Regularized Recursive Least Squares Algorithm

Intuitively, any estimation process can profit enormously from prior knowledge. Incorporating prior knowledge into the adaptive filtering problem is typically done by means of regularization. This chapter gives a systematic consideration for regularization strategies exploiting sparseness for the identification of acoustic room impulse responses specifically for multichannel systems. The main findings of this chapter have been presented in [1]. The high convergence rates achieved by the algorithm derived in this chapter build the motivation for the subsequent chapters of this book.
Karim Helwani

Chapter 4. Sparse Representation of Multichannel Acoustic Systems

In the previous chapter we highlighted the improvement of the convergence rate of Newton based adaptive algorithms by systematically exploiting the sparseness of the system. In the present chapter we will concentrate on a special form of sparsity namely, the diagonal sparsity.
Karim Helwani

Chapter 5. Unique System Identification from Projections

The presented multichannel adaptive filtering algorithms from the previous chapters aim at spatio-temporal decoupling of the signals by suitably chosen transformations.
Karim Helwani

Practical Aspects

Frontmatter

Chapter 6. Geometrical Constraints

Fortunately, multichannel systems entail possibilities to support an acoustic echo canceler, e.g., in [6] the directivity control, offered by microphone arrays, was exploited aiming at suppressing the short-range acoustic feedback from the loudspeakers to the array output, resulting in lower acoustic echo. Later in this chapter, it will be shown, how acoustic multichannel reproduction systems can make a significant contribution to reduce the acoustic echo.
Karim Helwani

Chapter 7. Acoustic Echo Suppression

In acoustic echo control, residual echo suppressors, originally introduced in a heuristic way, are typically employed after the actual system identification-based AEC in order to meet the requirements for a high attenuation of the echoes in practical applications. The approach presented in this chapter addresses both the distortion and double talk problems introduced by typical echo suppressores. In order to limit the signal distortion to a minimum in AES systems, a novel two-stage approach which explicitly constrains the near-end signal is presented.
Karim Helwani

Chapter 8. Conclusion and Outlook

This chapter concludes the book giving an overview of the work and highlighting the research lines opened by the presented findings for future research.
Karim Helwani

Backmatter

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