Search

A fundamental requirement of microphone arrays is the capability of instantaneously locating and continuously tracking a speech sound source. The problem is challenging in practice due to the fact that speech is a nonstationary random process with a wideband spectrum, and because of the simultaneous presence of noise, room reverberation, and other interfering speech sources. This Chapter presents an overview of the research and development on this technology in the last three decades. Focusing on a two-stage framework for speech source localization, we survey and analyze the state-of-the-art time delay estimation (TDE) time delay estimation (TDE) and source localization source localization algorithms. This chapter is organized into two sections. In Sect. 51.2, we will study the TDE problem and review a number of cutting-edge TDE algorithms, ranging from the generalized cross-correlation methods to blind multichannel-identification-based algorithms and the second-order statistics-based multichannel cross-correlation coefficient method to the higher-order statistics-based entropy-minimization approach. In Sect. 51.3, we will investigate the source localization problem from the perspective of estimation theory. The emphasis is on least-squares estimators with closed-form estimates. The spherical intersection, spherical interpolation, and linear-correction spherical interpolation algorithms will be presented. [ABSTRACT FROM AUTHOR]

The existence of noise is inevitable. In all applications that are related to voice and speech, from sound recording, telecommunications, and telecollaborations, to human-machine interfaces, the signal of interest that is picked up by a microphone is generally contaminated by noise. As a result, the microphone signal has to be cleaned up with digital signal-processing tools before it is stored, analyzed, transmitted, or played out. The cleaning process, which is often referred to as either noise reduction or speech enhancement, has attracted a considerable amount of research and engineering attention for several decades. Remarkable advances have already been made, and this area is continuing to progress, with the aim of creating processors that can extract the desired speech signal as if there is no noise. This chapter presents a methodical overview of the state of the art of noise-reduction algorithms. Based on their theoretical origin, the algorithms are categorized into three fundamental classes: filtering techniques, spectral restoration, and model-based methods. We outline the basic ideas underlying these approaches, discuss their characteristics, explain their intrinsic relationships, and review their advantages and disadvantages. [ABSTRACT FROM AUTHOR]

Room reverberation is one of the two major causes (the other is background noise) of speech degradation and there has been an increasing need for speech dereverberation in various speech processing and communication applications. Although it has been studied for decades, speech dereverberation remains a challenging problem from both a theoretical and practical perspective. This chapter provides a methodical overview of speech dereverberation algorithms. They are classified into three categories: source model-based speech enhancement algorithms for dereverberation, separation of speech and reverberation via homomorphic transformation, and speech dereverberation by channel inversion and equalization. We outline the basic ideas behind these approaches, explain the assumptions they make, and discuss their characteristics as well as performance. [ABSTRACT FROM AUTHOR]

Blind multichannel identification was first introduced in the blind multichannel identification mid 1970s and initially studied in the communication society with the intention of designing more-efficient communication systems by avoiding a training phase. Recently this idea has become increasingly interesting for acoustics and speech processing research, driven by the fact that in most acoustic applications for speech processing and communication very little or nothing is known about the source signals. Since human ears have an extremely wide dynamic range and are much more sensitive to weak tails of the acoustic impulse responses, these impulse responses need to be modeled using fairly long filters. Attempting to identify such a multichannel system blindly with a batch method involves intensive computational complexity. This is not just bad system design, but technically rather implausible, particularly for real-time systems. Therefore, adaptive blind multichannel identification algorithms are favorable and pragmatically useful. This chapter describes some fundamental issues in blind multichannel identification and reviews a number of state-of-the-art adaptive algorithms. [ABSTRACT FROM AUTHOR]

Wiener filter The Wiener filter, named after its inventor, has been an extremely useful tool since its invention in the early 1930s. This optimal filter is not only popular in different aspects of speech processing but also in many other applications. This chapter presents the most fundamental results of the Wiener theory with an emphasis on the Wiener-Hopf equations, which are not convenient to solve in practice. An alternative approach to solving these equations directly is the use of an adaptive filter, which is why this work also describes the most classical adaptive algorithms that are able to converge, in a reasonable amount of time, to the optimal Wiener filter. [ABSTRACT FROM AUTHOR]

Linear prediction linear prediction (LP) plays a fundamental role in all aspects of speech. Its use seems natural and obvious in this context since for a speech signal the value of its current sample can be well modeled as a linear combination of its past values. In this chapter, we attempt to present the most important ideas on linear prediction. We derive the principal results, widely recognized by speech experts, in a very intuitive way without sacrificing mathematical rigor. [ABSTRACT FROM AUTHOR]

In this chapter, we described four signal models for acoustic systems according to the number of inputs and outputs. The models were presented in both the time and frequency domains and will be frequently used throughout the rest of this book. The characteristics of acoustic channels were investigated and covered not only conventionally well-recognized features (e.g., linearity and shift-invariance, FIR, time-varying channels, frequency selectivity, invertibility, etc.) but also those characteristics that have not received adequate attention until very recently (like multichannel diversity, the common-zero problem, and sparseness). At the end of this chapter, we discussed facilities for directly measuring acoustic impulse responses and the image method for their simulation. [ABSTRACT FROM AUTHOR]