AES E-Library

Co-talker Separation Using the 'Cocktail Party Effect'

An artificial neural network (ANN) speech-classifier-controlled iterative filtering system is described, which simulates the cocktail party effect for speech separation. The ANN speech classifier controls a modified iterative Wiener filter to cancel the interference by setting the filter's parameter's and the convergence criterion for the iteration. The proposed system has been employed successfully with multiple-microphone speech acquisition systems for co-talker speech separation. The simulation results have shown that the iterative processing controlled by the neural network consistently provides speech of good quality and intelligibility.

Authors: Cao, Yuchang; Sridharan, Sridha; Moody, Miles
Affiliation: Speech Research Laborawry, Signal Processing Research Centre, School of Electrical and Electronic Systems, Engineering, Queensland University of Technology, Brisband, Australia
JAES Volume 44 Issue 12 pp. 1084-1096; December 1996 Import into BibTeX

Click to purchase paper or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $20 for non-members, $5 for AES members and is free for E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD aes5)   /jrnl8997/1996/8658.pdf

Start a discussion about this paper!