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Lecture Notes in Electrical Engineering #3: Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment

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Lecture Notes in Electrical Engineering #3: Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment Cover

 

Synopses & Reviews

Publisher Comments:

Speech is a natural and therefore privileged communication modality. Safety and convenience issues require hands-free, eyes-free speech-based human-computer interfaces to manipulate complex functionalities and devices. For example, in cars, applications include entertainment, telephony as well as more advanced functions such as automatic spoken language dialog systems for in-vehicle navigation. With a seamless speech input, such interfaces bring an increased comfort but have to face several issues: degradation of the signal-to-noise ratio (SNR) at the microphone, reverberated speech signal, and, above all, the presence of interferences. The interferences, such as speech from the co-driver, can greatly hamper the performance of the speech recognition component, which is crucial for dialog applications. Especially for overlaid speech, the separation of the target speaker from the interferer represent a particular challenge. Time-domain Beamforming and Convolutive Blind Source Separation addresses the problem of separating spontaneous multi-party speech by way of microphone arrays (beamformers) and adaptive signal processing techniques. While existing techniques requires a Double-Talk Detector (DTD) that interrupts the adaptation when the target is active, the described method addresses the separation problem using continuous, uninterrupted adaptive algorithms. The advantage of such an approach is twofold: Firstly, the algorithm development is much simpler since no detection mechanism needs to be designed and no threshold to be tuned. Secondly, the performance can be improved due to the adaptation during periods of double-talk. The book is organized in three parts, roughly described as follows: The first line of attack, termed implicit beamforming, is built upon the classical supervised beamforming, i.e. it requires the position of the target speaker to be known. Using a time-varying pseudo-optimal step-size that takes over the adaptation control, a continuous adaptive algorithm is obtained. Experimentally, the performance of this algorithm appears to be sufficient if the microphones are oriented adequately. However, in general, more sophisticated Blind Source Separation (BSS) techniques are required. In the second part, the time-domain BSS method (Buchner et al., 2005) exploiting second-order statistics of the source signals is considered. This method is based on the natural gradient and limited to square systems with an equal number of sources and microphones. Introducing the concept of partial separation, a novel approach is proposed to remove this restriction of the natural gradient. The Sylvester-based representation of the separation system allows a very concise derivation of second-order BSS algorithms in the time-domain but cannot be directly implemented. Revisiting the natural gradient in the z-domain, this implementation issue is clarified. Furthermore, the convergence and stability of BSS is discussed from a theoretical point of view, and its properties are compared to those of supervised beamforming. Finally, combinations of beamforming and BSS are presented leading to already known, but also novel algorithms. The underlying idea is the following: if the position of the target speaker (the driver) is known in advance, a purely blind approach, which does not exploit this information, seems sub-optimal. Therefore, an emphasis is placed on the development of an algorithm that combines the benefits of both approaches. It outperforms BSS and removes the need for a DTD and allows for a continuous adaptation, even during double-talk.  The book is written is a concise manner and an effort has been made such that all presented algorithms can be straightforwardly implemented by the reader. All experimental results have been obtained with real in-car microphone recordings involving simultaneous speech of the driver and the co-driver, as opposed to computer-generated simulations. Experiments with background noise have been carried out in order to assess the robustness of the considered methods in noisy conditions.

Synopsis:

Time-Domain Beamforming and Blind Source Separation addresses the problem of separating spontaneous multi-party speech by way of microphone arrays (beamformers) and adaptive signal processing techniques. While existing techniques require a Double-Talk Detector (DTD) that interrupts the adaptation when the target is active, the described method addresses the separation problem using continuous, uninterrupted adaptive algorithms. With this approach, algorithm development is much simpler since no detection mechanism needs to be designed and needs no threshold to be tuned. Also, performance can be improved due to the adaptation during periods of double-talk. The authors use two techniques to achieve these results: implicit beamforming, which requires the position of the target speaker to be known; and time-domain blind-source separation (BSS), which exploits second-order statistics of the source signals. In combination, beamforming and BSS can be used to develop novel algorithms. Emphasis is placed on the development of an algorithm that combines the benefits of both approaches. The book presents experimental results obtained with real in-car microphone recordings involving simultaneous speech of the driver and the co-driver. In addition, experiments with background noise have been carried out in order to assess the robustness of the considered methods in noisy conditions.

Synopsis:

This book addresses the problem of separating spontaneous multi-party speech by way of microphone arrays (beamformers) and adaptive signal processing techniques. It is written is a concise manner and an effort has been made such that all presented algorithms can be straightforwardly implemented by the reader. All experimental results have been obtained with real in-car microphone recordings involving simultaneous speech of the driver and the co-driver.

Table of Contents

1 Introduction 1 1.1 Existing approaches: a brief overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Scope and objective of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Non-adaptive stationary beamforming 5 2.1 Problemand notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 The space-frequency response for omni-directional microphones . . . . . . . . . . . . . . . 6 2.3 Minimum VarianceDistortionless Response (MVDR) . . . . . . . . . . . . . . . . . . . . . 8 2.4 Data-independent beamformers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4.1 The delay-and-sumbeamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4.2 TheMVDR null beamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.5 Statistically optimumMVDR beamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.6 FromMVDR to Generalized Sidelobe Canceller (GSC) . . . . . . . . . . . . . . . . . . . . 12 2.7 The target signal cancellation problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.7.1 The power-inversion effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.7.2 Robust versions of the GSC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.8 Use of directionalmicrophones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.8.1 Directionalmicrophones with the same orientation . . . . . . . . . . . . . . . . . . 16 2.8.2 Directionalmicrophones oriented to the sources . . . . . . . . . . . . . . . . . . . . 16 2.9 Experiments under stationary acoustic conditions . . . . . . . . . . . . . . . . . . . . . . . 182.9.1 Experiments with the mirror array . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.9.2 Experiments with the cocooning array . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.10 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3 Implicit adaptation control for beamforming 27 3.1 Adaptive interference canceller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Implicit adaptation control with a pseudo-optimal step-size . . . . . . . . . . . . . . . . . 29 3.3 ILMS transient behavior and stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.1 Transient convergence and divergence . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.2 About the stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4 Robustness improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.1 Experiments with the mirror array . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.5.2 Experiment with the cocooning array . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.6 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4 Second-Order Blind Source Separation 43 4.1 Problemand notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.1.1 Froma scalar to a convolutivemixture model . . . . . . . . . . . . . . . . . . . . . 44 4.1.2 Separation constraints and degrees of freedom. . . . . . . . . . . . . . . . . . . . . 46 4.2 Nonstationarity and source separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2.1 The insufficiency of decorrelation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 i 4.2.2 Nonstationarity-based separation cost function. . . . . . . . . . . . . . . . . . . . . 47 4.3 Gradient-basedminimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.1 Standard gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.2 Natural gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.4 Natural gradient algorithmfor non-square systems . . . . . . . . . . . . . . . . . . . . . . 50 4.5 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 5 Implementation Issues in Blind Source Separation 53 5.1 Convolutive Natural Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.1.1 Gradient in the Sylvestermanifold . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.1.2 From matrices to z-transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.1.3 Self-closed and non-self-closed natural gradients . . . . . . . . . . . . . . . . . . . . 56 5.1.4 From z-transforms back to the time domain . . . . . . . . . . . . . . . . . . . . . . 57 5.1.5 Application to second-order BSS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.1.6 Discussion: Which natural gradient is best? . . . . . . . . . . . . . . . . . . . . . . 60 5.2 Online adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.2.1 Blockwise batch BSS algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.2.2 Sample-wise BSS algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.3.1 Experiments with the mirror array . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.3.2 Experiments with the cocooning array . . . . . . . . . . . . . . . . . . . . . . . . . 66 5.3.3 Comparison with other BSS algorithms in the frequency domain . . . . . . . . . . 66 5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6 Blind Source Separation: Convergence and Stability 71 6.1 Global convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.1.1 Difficulty of a global convergence analysis . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.2 Convergence analysis for a simplified algorithm . . . . . . . . . . . . . . . . . . . . 73 6.2 Local stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7 Comparison of Beamforming and Blind Source Separation 77 7.1 System identification vs. interference cancellation . . . . . . . . . . . . . . . . . . . . . . . 77 7.2 Properties of the cost function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.2.1 Convergence of the gradient descent . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.2.2 Statistical efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.3 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 7.3.1 NLMS complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 7.3.2 BSS complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 7.3.3 NLMS vs. BSS complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.3.4 Online BSS algorithm in the special case N =2 . . . . . . . . . . . . . . . . . . . . 86 7.4 Experimental comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 7.5 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 8 Combining Blind Source Separation and Beamforming 91 8.1 Existing combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 8.2 BSS and geometric prior information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 8.2.1 Causality information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 8.2.2 Prior information on the source direction of arrival . . . . . . . . . . . . . . . . . . 93 8.2.3 Geometric information at the initialization . . . . . . . . . . . . . . . . . . . . . . 95 8.2.4 Geometric information as a soft constraint . . . . . . . . . . . . . . . . . . . . . . . 96 8.2.5 Geometric information as a preprocessing . . . . . . . . . . . . . . . . . . . . . . . 99 8.3 Combining BSS and the power criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8.4 Combining BSS with geometric prior information and the power criterion . . . . . . . . . 102 ii 8.5 Experimental results on automatic speech recognition . . . . . . . . . . . . . . . . . . . . 104 8.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 A Experimental setups 109 A.1 Mirror array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 A.2 Cocooning array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 A.3 Acoustic characteristics of the car cabin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 B The RGSC according to Hoshuyama et al. 113 B.1 RGSC for the mirror array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 B.2 RGSC for the cocooning array. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 B.3 Experimental comparison: GSC vs. RGSC. . . . . . . . . . . . . . . . . . . . . . . . . . . 115 B.3.1 Mirror array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 B.3.2 Cocooning array. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 B.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 C Stability Analysis 119 C.1 Mixing and separationmodels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 C.2 Linearization of the BSS updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 C.3 Local stability conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Bibliography 125 iii

Product Details

ISBN:
9781441943323
Author:
Bourgeois, Julien
Publisher:
Springer
Author:
Minker, Wolfgang
Subject:
Electricity-General Electricity
Subject:
Electronics - General
Subject:
Signal, Image and Speech Processing
Subject:
Communications Engineering, Networks
Subject:
Acoustics
Subject:
Electrical engineering
Copyright:
Edition Description:
Softcover reprint of hardcover 1st ed. 2009
Series:
Lecture Notes in Electrical Engineering
Series Volume:
3
Publication Date:
20101123
Binding:
TRADE PAPER
Language:
English
Pages:
240
Dimensions:
235 x 155 mm 369 gr

Related Subjects


Reference » Science Reference » Technology
Science and Mathematics » Electricity » General Electricity
Science and Mathematics » Electricity » General Electronics
Science and Mathematics » Physics » Acoustics

Lecture Notes in Electrical Engineering #3: Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment New Trade Paper
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Product details 240 pages Springer - English 9781441943323 Reviews:
"Synopsis" by , Time-Domain Beamforming and Blind Source Separation addresses the problem of separating spontaneous multi-party speech by way of microphone arrays (beamformers) and adaptive signal processing techniques. While existing techniques require a Double-Talk Detector (DTD) that interrupts the adaptation when the target is active, the described method addresses the separation problem using continuous, uninterrupted adaptive algorithms. With this approach, algorithm development is much simpler since no detection mechanism needs to be designed and needs no threshold to be tuned. Also, performance can be improved due to the adaptation during periods of double-talk. The authors use two techniques to achieve these results: implicit beamforming, which requires the position of the target speaker to be known; and time-domain blind-source separation (BSS), which exploits second-order statistics of the source signals. In combination, beamforming and BSS can be used to develop novel algorithms. Emphasis is placed on the development of an algorithm that combines the benefits of both approaches. The book presents experimental results obtained with real in-car microphone recordings involving simultaneous speech of the driver and the co-driver. In addition, experiments with background noise have been carried out in order to assess the robustness of the considered methods in noisy conditions.
"Synopsis" by , This book addresses the problem of separating spontaneous multi-party speech by way of microphone arrays (beamformers) and adaptive signal processing techniques. It is written is a concise manner and an effort has been made such that all presented algorithms can be straightforwardly implemented by the reader. All experimental results have been obtained with real in-car microphone recordings involving simultaneous speech of the driver and the co-driver.
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