A batch algorithm for blind source separation of acoustic signals using ICA and time-frequency masking

Authors:
Eugen Hoffmann;Dorothea Kolossa;Reinhold Orglmeister
Affiliations:
Berlin University of Technology, Electronics and Medical Signalporcessing Group, Berlin, Germany;Berlin University of Technology, Electronics and Medical Signalporcessing Group, Berlin, Germany;Berlin University of Technology, Electronics and Medical Signalporcessing Group, Berlin, Germany
Venue:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

Citing 2
Cited 2

High-order contrasts for independent component analysis

Neural Computation
Blind separation of speech mixtures via time-frequency masking

IEEE Transactions on Signal Processing

Time Frequency Masking Strategy for Blind Source Separation of Acoustic Signals Based on Optimally-Modified LOG-Spectral Amplitude Estimator

ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Independent component analysis and time-frequency masking for speech recognition in multitalker conditions

EURASIP Journal on Audio, Speech, and Music Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The problem of Blind Source Separation (BSS) of convolved acoustic signals is of great interest for many classes of applications such as in-car speech recognition, hands-free telephony or hearing devices. Due to the convolutive mixing process, the source separation is performed in the frequency domain, using Independent Component Analysis (ICA). However the quality of solution of the ICA-algorithms can be improved by applying time-frequency masking. In this paper we present a batch-algorithm for time-frequency masking using the time-frequency structure of separated signals.