Automatic identification of sound source position employing neural networks and rough sets

Abstract

Methods for the identification of direction of the incoming acoustical signal in the presence of noise and reverberation are investigated. Since the problem is a non-deterministic one, thus applications of two learning algorithms, namely neural networks and rough sets are developed to solve it. Consequently, two sets of parameters have been formulated in order to discern target source from unwanted sound source position and then processed by learning algorithms. The applied feature extraction methods are discussed, training processes are described and obtained sound source localizing results are demonstrated and compared.

Complexity simplification for an identification audio model in mobile robots navigation

“…So far, usually for this purpose overall odometer, camera, infrared sensor, ultra sonic sensor, mechanical wave and laser are mainly used. Nowadays the role of acoustic perception in autonomous robots, intelligent buildings and industrial environments is increasingly important and in the literature there are different works [6] [7].…”

Robust Audio Localization for Mobile Robots in Industrial Environments

“…If the target is not in the visual field of camera or the lighting condition is poor, the visual localization system of the mobile robot cannot work effectively. Nowadays, the role of acoustic perception in autonomous robots, intelligent buildings and industrial environments is increasingly important and in the literature there are different works (Yang et al, 2007); (Mumolo et al, 2003); (Csyzewski, 2003). Comparing to the study on visual perception, the study on auditory is still in its infancy stage.…”

Robot localization method by acoustical signal identification

“…So far, some previous investigations of robot localization by audio sensors performed by different authors [1] [2] have obtained methods of self-localization with acceptable results but with important drawbacks, such as considerable errors or uncertainty in localization.…”