Blind Source Separation Under a Dynamic Acoustic Environment

Blind Source Separation for Speech Application Under Real Acoustic Environment

A hands-free speech recognition system [1] is essential for the realization of an intuitive, unconstrained, and stress-free human-machine interface, where users can talk naturally because they require no microphone in their hands. In this system, however, since noise and reverberation always degrade speech quality, it is difficult to achieve high recognition performance, compared with the case ...

Blind Source Separation based on the Estimation for the Number of the Blind Sources under a Dynamic Acoustic Environment

Independent component analysis can estimate unknown source signals from their mixtures under the assumption that the source signals are statistically independent. However, in a real environment, the separation performance is often deteriorated because the number of the source signals is different from that of the sensors. In this paper, we propose an estimation method for the number of the sour...

Developing A System For Blind Acoustic Source Localization And Separation

DEVELOPING A SYSTEM FOR BLIND ACOUSTIC SOURCE LOCALIZATION ANDSEPARATIONbyRAGHAVENDRA KULKARNIAugust 2013Advisor: Dr. Sean F. WuMajor: Mechanical EngineeringDegree: Master of Science This thesis presents innovate methodologies for locating, extracting, and separatingmultiple incoherent sound sources in three-dimensional (3D) space; and applications of the time<lb...

Dynamic signal mixtures and blind source separation

Methods for blind source separation (BSS) from linear instantaneous signal mixtures have drawn a significant attention due to their ability to recover original independent non-Gaussian sources without analyzing their temporal statistics. Hence, original voices or images (modulo permutation and linear scaling) are extracted from their mixtures without modeling the dynamics of the signals. The ty...

Acoustic Blind Source Separation in Reverberant and Noisy Environments