Analysis and improvement of speech/music classification for 3GPP2 SMV based on GMM

Title
Analysis and improvement of speech/music classification for 3GPP2 SMV based on GMM
Authors
Song, J.H.; Lee, K.H.; Chang, J.H.; Kim, J.K.; Kim, N.S.
Keywords
Gaussian mixture model (GMM), selectable mode vocoder (SMV)
Issue Date
2008
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
In this letter, a novel approach is proposed to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the Gaussian mixture model (GMM). An in-depth analysis of the features and classification method adopted in the conventional SMV is performed. Feature vectors applied to the GMM are then selected from the relevant parameters of the SMV for efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme implemented in the SMV.
URI
http://dspace.inha.ac.kr/handle/10505/1835
ISSN
1070-9908
Appears in Collections:
College of Information Technology & Engineering (IT공과대학) > Electronic Engineering (전자공학) > Journal Papers, Reports(전자공학 논문, 보고서)
Files in This Item:
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