Title :
Speech separation by simulating the cocktail party effect with a neural network controlled Wiener filter
Author :
Cao, Yuchang ; Sridharan, Sridha ; Moody, Miles
Author_Institution :
Sch. of Electr. & Electron. Syst. Eng., Queensland Univ. of Technol., Brisbane, Qld., Australia
Abstract :
A novel speech separation structure which simulates the cocktail party effect using a modified iterative Wiener filter and a multi-layer perceptron neural network is presented. The neural network is used as a speaker recognition system to control the iterative Wiener filter. The neural network is a modified perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The proposed technique has been successfully used for speech separation when the interference is competing speech or broad band noise
Keywords :
Wiener filters; cepstral analysis; digital simulation; feature extraction; filtering theory; iterative methods; linear predictive coding; multilayer perceptrons; simulation; speaker recognition; speech enhancement; speech processing; LPC cepstral analysis; broadband noise; cocktail party effect simulation; feature data extraction; hidden layer; modified iterative Wiener filter; multilayer perceptron neural network; neural network controlled Wiener filter; speaker recognition system; speech enhancement; speech interference; speech separation; Control systems; Data mining; Feature extraction; Linear predictive coding; Multi-layer neural network; Multilayer perceptrons; Neural networks; Speaker recognition; Speech enhancement; Wiener filter;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Conference_Location :
Munich
Print_ISBN :
0-8186-7919-0
DOI :
10.1109/ICASSP.1997.595489
