Statt 96,99 €**
84,99 €
versandkostenfrei*

inkl. MwSt.
**Früherer Preis
Sofort lieferbar
42 °P sammeln
  • Gebundenes Buch

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.…mehr

Produktbeschreibung
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.

  • Produktdetails
  • Lecture Notes in Electrical Engineering 3
  • Verlag: Springer / Springer US / Springer, Berlin
  • Artikelnr. des Verlages: 11736240, 978-0-387-68835-0
  • Erscheinungstermin: 14. April 2009
  • Englisch
  • Abmessung: 241mm x 159mm x 22mm
  • Gewicht: 1140g
  • ISBN-13: 9780387688350
  • ISBN-10: 0387688358
  • Artikelnr.: 21703472
Autorenporträt
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.
Inhaltsangabe
1 Introduction 11.1 Existing approaches: a brief overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Scope and objective of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Non-adaptive stationary beamforming 52.1 Problemand notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 The space-frequency response for omni-directional microphones . . . . . . . . . . . . . . . 62.3 Minimum VarianceDistortionless Response (MVDR) . . . . . . . . . . . . . . . . . . . . . 82.4 Data-independent beamformers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.4.1 The delay-and-sumbeamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.4.2 TheMVDR null beamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.5 Statistically optimumMVDR beamformer . . . . . . . . . . . . . . . . . . 222.10 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Implicit adaptation control for beamforming 273.1 Adaptive interference canceller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.2 Implicit adaptation control with a pseudo-optimal step-size . . . . . . . . . . . . . . . . . 293.3 ILMS transient behavior and stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.3.1 Transient convergence and divergence . . . . . . . . . . . . . . . . . . . . . . . . . 313.3.2 About the stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.4 Robustness improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.5.1 Experiments with the mirror array . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.5.2 Experiment with the cocooning array . . . . . . . . . . . . . . . . . . . . . . . . . 383.6 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Second-Order Blind Source Separation 434.1 Problemand notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444.1.1 Froma scalar to a convolutivemixture model . . . . . . . . . . . . . . . . . . . . . 444.1.2 Separation constraints and degrees of freedom. . . . . . . . . . . . . . . . . . . . . 464.2 Nonstationarity and source separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.2.1 The insufficiency of decorrelation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47i4.2.2 Nonstationarity-based separation cost function. . . . . . . . . . . . . . . . . . . . . 474.3 Gradient-basedminimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.3.1 Standard gradient . . . . . . . . . . . . . . . . .