By Topic

A Binaural Scene Analyzer for Joint Localization and Recognition of Speakers in the Presence of Interfering Noise Sources and Reverberation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
May, T. ; Inst. of Phys., Univ. of Oldenburg, Oldenburg, Germany ; van de Par, S. ; Kohlrausch, A.

In this study, we present a binaural scene analyzer that is able to simultaneously localize, detect and identify a known number of target speakers in the presence of spatially positioned noise sources and reverberation. In contrast to many other binaural cocktail party processors, the proposed system does not require a priori knowledge about the azimuth position of the target speakers. The proposed system consists of three main building blocks: binaural localization, speech source detection, and automatic speaker identification. First, a binaural front-end is used to robustly localize relevant sound source activity. Second, a speech detection module based on missing data classification is employed to determine whether detected sound source activity corresponds to a speaker or to an interfering noise source using a binary mask that is based on spatial evidence supplied by the binaural front-end. Third, a second missing data classifier is used to recognize the speaker identities of all detected speech sources. The proposed system is systematically evaluated in simulated adverse acoustic scenarios. Compared to state-of-the art MFCC recognizers, the proposed model achieves significant speaker recognition accuracy improvements.

Published in:

Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:20 ,  Issue: 7 )
Biometrics Compendium, IEEE
RFIC Virtual Journal, IEEE
RFID Virtual Journal, IEEE