Analysis and Improvement of Speech/Music Classification for 3GPP2 SMV Based on GMM

Author

Song, Ji-hyun ; Lee, Kye-Hwan ; Chang, Joon-Hyuk ; Kim, Jong Kyu ; Kim, Nam Soo

Author_Institution

Inha Univ., Incheon

Volume

15

fYear

2008

fDate

6/30/1905 12:00:00 AM

Firstpage

103

Lastpage

106

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.

Keywords

Gaussian processes; audio signal processing; signal classification; vocoders; Gaussian mixture model; feature vectors; music classification; selectable mode vocoder; speech classification; Adaptive filters; Bandwidth; Counting circuits; Linear predictive coding; Multiple signal classification; Performance analysis; Signal processing algorithms; Speech analysis; Speech codecs; Vocoders; Gaussian mixture model (GMM); selectable mode vocoder (SMV); speech/music classification;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2007.911184

Filename

4418410