Advances in Modern Blind Signal Separation Algorithms: Theory and Applications

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Morgan & Claypool Publishers, 2010 - Technology & Engineering - 88 pages
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With human-computer interactions and hands-free communications becoming overwhelmingly important in the new millennium, recent research efforts have been increasingly focusing on state-of-the-art multimicrophone signal processing solutions to improve speech intelligibility in adverse environments. One such prominent statistical signal processing technique is blind signal separation (BSS). BSS was first introduced in the early 1990s and quickly emerged as an area of intense research activity showing huge potential in numerous applications. BSS comprises the task of `blindly' recovering a set of unknown signals, the so-called sources from their observed mixtures, based on very little to almost no prior knowledge about the source characteristics or the mixing structure. The goal of BSS is to process multi-sensory observations of an inaccessible set of signals in a manner that reveals their individual (and original) form, by exploiting the spatial and temporal diversity, readily accessible through a multi-microphone configuration. Proceeding blindly exhibits a number of advantages, since assumptions about the room configuration and the source-to-sensor geometry can be relaxed without affecting overall efficiency.

This booklet investigates one of the most commercially attractive applications of BSS, which is the simultaneous recovery of signals inside a reverberant (naturally echoing) environment, using two (or more) microphones. In this paradigm, each microphone captures not only the direct contributions from each source, but also several reflected copies of the original signals at different propagation delays. These recordings are referred to as the convolutive mixtures of the original sources. The goal of this booklet in the lecture series is to provide insight on recent advances in algorithms, which are ideally suited for blind signal separation of convolutive speech mixtures. More importantly, specific emphasis is given in practical applications of the developed BSS algorithms associated with real-life scenarios. The developed algorithms are put in the context of modern DSP devices, such as hearing aids and cochlear implants, where design requirements dictate low power consumption and call for portability and compact size. Along these lines, this booklet focuses on modern BSS algorithms which address (1) the limited amount of processing power and (2) the small number of microphones available to the end-user.
 

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Contents

Fundamentals of blind signal separation
1
12 APPLICATIONS
2
13 INSTANTANEOUS MIXING MODEL
3
14 ASSUMPTIONS
4
15 INDEPENDENT PRINCIPAL COMPONENT ANALYSIS
7
16 ILLUSTRATION OF PCA AND ICA
8
17 ACOUSTICS
9
18 CONVOLUTIVE MIXING MODEL
13
26 PERFORMANCE MEASURES
42
27 SUMMARY
45
Application of blind signal processing strategies to noise reduction for the hearingimpaired
47
312 HEARING AIDS
48
313 COCHLEAR IMPLANTS
49
32 SPEECH INTELLIGIBILITY IN NOISE
50
33 NOISE REDUCTION STRATEGIES FOR HEARINGIMPAIRED LISTENERS
53
332 2M2BEAM STRATEGY
54

19 SUMMARY
19
Modern blind signal separation algorithms
23
222 ENTROPY MAXIMIZATION
24
23 ADAPTIVE ALGORITHMS FOR BSS
27
232 NATURAL OR RELATIVE GRADIENT
28
24 PARAMETRIC SOURCE DENSITY MODELS FOR BSS
30
242 MOMENT MATCHING ESTIMATORS
31
243 PARAMETRIC SCORE FUNCTIONS
32
25 BLIND SIGNAL SEPARATION STRATEGIES FOR CONVOLUTIVE MIXTURES
35
251 CONVOLUTIVE BSS BASED ON SECONDORDER STATISTICS
36
252 CONVOLUTIVE BSS BASED ON HIGHERORDER STATISTICS
38
253 SCALING AND PERMUTATION
41
34 SPEECH INTELLIGIBILITY STUDIES WITH HEARINGIMPAIRED LISTENERS
56
342 PROCEDURE
57
343 STIMULI
58
345 REVERBERANT CONDITIONS
59
35 RESULTS DISCUSSION
61
352 EFFECTS OF TRAINING ON SPEECH INTELLIGIBILITY
62
353 REVERBERANT CONDITIONS
67
Conclusions and future challenges
71
Bibliography
75
Authors Biographies
87
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