Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Support Vector Machine
Summary :
In this letter, we propose a novel approach to speech/music classification based on the support vector machine (SVM) to improve the performance of the 3GPP2 selectable mode vocoder (SMV) codec. We first analyze the features and the classification method used in real time speech/music classification algorithm in SMV, and then apply the SVM for enhanced speech/music classification. For evaluation of performance, we compare the proposed algorithm and the traditional algorithm of the SMV. The performance of the proposed system is evaluated under the various environments and shows better performance compared to the original method in the SMV.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.2 pp.630-632
- Publication Date
- 2009/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.630
- Type of Manuscript
- LETTER
- Category
- Speech and Hearing
Authors
Keyword
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Sang-Kyun KIM, Joon-Hyuk CHANG, "Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Support Vector Machine" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 2, pp. 630-632, February 2009, doi: 10.1587/transfun.E92.A.630.
Abstract: In this letter, we propose a novel approach to speech/music classification based on the support vector machine (SVM) to improve the performance of the 3GPP2 selectable mode vocoder (SMV) codec. We first analyze the features and the classification method used in real time speech/music classification algorithm in SMV, and then apply the SVM for enhanced speech/music classification. For evaluation of performance, we compare the proposed algorithm and the traditional algorithm of the SMV. The performance of the proposed system is evaluated under the various environments and shows better performance compared to the original method in the SMV.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.630/_p
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@ARTICLE{e92-a_2_630,
author={Sang-Kyun KIM, Joon-Hyuk CHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Support Vector Machine},
year={2009},
volume={E92-A},
number={2},
pages={630-632},
abstract={In this letter, we propose a novel approach to speech/music classification based on the support vector machine (SVM) to improve the performance of the 3GPP2 selectable mode vocoder (SMV) codec. We first analyze the features and the classification method used in real time speech/music classification algorithm in SMV, and then apply the SVM for enhanced speech/music classification. For evaluation of performance, we compare the proposed algorithm and the traditional algorithm of the SMV. The performance of the proposed system is evaluated under the various environments and shows better performance compared to the original method in the SMV.},
keywords={},
doi={10.1587/transfun.E92.A.630},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Support Vector Machine
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 630
EP - 632
AU - Sang-Kyun KIM
AU - Joon-Hyuk CHANG
PY - 2009
DO - 10.1587/transfun.E92.A.630
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2009
AB - In this letter, we propose a novel approach to speech/music classification based on the support vector machine (SVM) to improve the performance of the 3GPP2 selectable mode vocoder (SMV) codec. We first analyze the features and the classification method used in real time speech/music classification algorithm in SMV, and then apply the SVM for enhanced speech/music classification. For evaluation of performance, we compare the proposed algorithm and the traditional algorithm of the SMV. The performance of the proposed system is evaluated under the various environments and shows better performance compared to the original method in the SMV.
ER -
English
