Blind Speech Separation

This is the first book to provide a cutting edge reference to the fascinating topic of blind source separation (BSS) for convolved speech mixtures. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theo...

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Bibliographic Details
Call Number:Libro Electrónico
Corporate Author: SpringerLink (Online service)
Other Authors: Makino, Shoji (Editor), Lee, Te-Won (Editor), Sawada, Hiroshi (Editor)
Format: Electronic eBook
Language:Inglés
Published: Dordrecht : Springer Netherlands : Imprint: Springer, 2007.
Edition:1st ed. 2007.
Series:Signals and Communication Technology,
Subjects:
Signal processing.
Telecommunication.
Signal, Speech and Image Processing .
Communications Engineering, Networks.
Microwaves, RF Engineering and Optical Communications.
Online Access:Texto Completo
Table of Contents:
  • Multiple Microphone Blind Speech Separation with ICA
  • Convolutive Blind Source Separation for Audio Signals
  • Frequency-Domain Blind Source Separation
  • Blind Source Separation using Space-Time Independent Component Analysis
  • TRINICON-based Blind System Identification with Application to Multiple-Source Localization and Separation
  • SIMO-Model-Based Blind Source Separation - Principle and its Applications
  • Independent Vector Analysis for Convolutive Blind Speech Separation
  • Relative Newton and Smoothing Multiplier Optimization Methods for Blind Source Separation
  • Underdetermined Blind Speech Separation with Sparseness
  • The DUET Blind Source Separation Algorithm
  • K-means Based Underdetermined Blind Speech Separation
  • Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and L1-Norm Minimization
  • Bayesian Audio Source Separation
  • Single Microphone Blind Speech Separation
  • Monaural Source Separation
  • Probabilistic Decompositions of Spectra for Sound Separation
  • Sparsification for Monaural Source Separation
  • Monaural Speech Separation by Support Vector Machines: Bridging the Divide Between Supervised and Unsupervised Learning Methods.

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