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Paper Information

Title: 

AUDIO CLASSIFICATION BY COMBINATION OF PERCEPTUAL AND CEPSTRAL REPRESENTATION USING NEURAL NETWORKS

Type: PAPER
Author(s): MORADMAND M.ALI*,SAED SALEHI ALI,ALMASGANJ FARSHAD
 
 *AMIRKABIR UNIVERSITY OF TECHNOLOGY - BIOMEDICAL FACULTY
 
Name of Seminar: IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING
Type of Seminar:  CONFERENCE
Sponsor:  ANJOMANE MOHANDESI PEZESHKI IRAN
Date:  2004Volume 11
 
 
Abstract: 

BY IMPROVING OF AUDIO SIGNAL PROCESSING RECENTLY, THERE IS TOO IMPORTANT TO CLASSIFY THESE SIGNALS BEFORE ADVANCE PROCESSING, AFTER CLASSIFYING AN AUDIO STREAM TO A CLASS WE CAN MAKE DECISION THAT WHAT KIND OF PROCESS SHOULD BE DONE FOR THIS SIGNAL. IN THIS ARTICLE WE PRESENT A ROBUST AUDIO CLASSIFIER WHICH CAN CLASSIFY AUDIO SIGNALS INTO SPEECH OR NON-SPEECH CLASSES, FOR THIS REASON FIRST WE SHOULD EXTRACT COMBINATION OF PERCEPTUAL AND CEPSTRAL FEATURES FROM EACH FRAME AND THEN BY USING FEED FORWARD NEURAL NETWORKS FOR EACH SIGNAL INTERVAL WE EXAMINE OUR TEST DATA IN THIS TWO CLASS.

 
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