IEICE TRANSACTIONS on Information
Improvement of SVM-Based Speech/Music Classification Using Adaptive Kernel Technique
Summary :
In this paper, we propose a way to improve the classification performance of support vector machines (SVMs), especially for speech and music frames within a selectable mode vocoder (SMV) framework. A myriad of techniques have been proposed for SVMs, and most of them are employed during the training phase of SVMs. Instead, the proposed algorithm is applied during the test phase and works with existing schemes. The proposed algorithm modifies a kernel parameter in the decision function of SVMs to alter SVM decisions for better classification accuracy based on the previous outputs of SVMs. Since speech and music frames exhibit strong inter-frame correlation, the outputs of SVMs can guide the kernel parameter modification. Our experimental results show that the proposed algorithm has the potential for adaptively tuning classifications of support vector machines for better performance.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.3 pp.888-891
- Publication Date
- 2012/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.888
- Type of Manuscript
- LETTER
- Category
- Speech and Hearing
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Chungsoo LIM, Joon-Hyuk CHANG, "Improvement of SVM-Based Speech/Music Classification Using Adaptive Kernel Technique" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 3, pp. 888-891, March 2012, doi: 10.1587/transinf.E95.D.888.
Abstract: In this paper, we propose a way to improve the classification performance of support vector machines (SVMs), especially for speech and music frames within a selectable mode vocoder (SMV) framework. A myriad of techniques have been proposed for SVMs, and most of them are employed during the training phase of SVMs. Instead, the proposed algorithm is applied during the test phase and works with existing schemes. The proposed algorithm modifies a kernel parameter in the decision function of SVMs to alter SVM decisions for better classification accuracy based on the previous outputs of SVMs. Since speech and music frames exhibit strong inter-frame correlation, the outputs of SVMs can guide the kernel parameter modification. Our experimental results show that the proposed algorithm has the potential for adaptively tuning classifications of support vector machines for better performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.888/_p
Copy
@ARTICLE{e95-d_3_888,
author={Chungsoo LIM, Joon-Hyuk CHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Improvement of SVM-Based Speech/Music Classification Using Adaptive Kernel Technique},
year={2012},
volume={E95-D},
number={3},
pages={888-891},
abstract={In this paper, we propose a way to improve the classification performance of support vector machines (SVMs), especially for speech and music frames within a selectable mode vocoder (SMV) framework. A myriad of techniques have been proposed for SVMs, and most of them are employed during the training phase of SVMs. Instead, the proposed algorithm is applied during the test phase and works with existing schemes. The proposed algorithm modifies a kernel parameter in the decision function of SVMs to alter SVM decisions for better classification accuracy based on the previous outputs of SVMs. Since speech and music frames exhibit strong inter-frame correlation, the outputs of SVMs can guide the kernel parameter modification. Our experimental results show that the proposed algorithm has the potential for adaptively tuning classifications of support vector machines for better performance.},
keywords={},
doi={10.1587/transinf.E95.D.888},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Improvement of SVM-Based Speech/Music Classification Using Adaptive Kernel Technique
T2 - IEICE TRANSACTIONS on Information
SP - 888
EP - 891
AU - Chungsoo LIM
AU - Joon-Hyuk CHANG
PY - 2012
DO - 10.1587/transinf.E95.D.888
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2012
AB - In this paper, we propose a way to improve the classification performance of support vector machines (SVMs), especially for speech and music frames within a selectable mode vocoder (SMV) framework. A myriad of techniques have been proposed for SVMs, and most of them are employed during the training phase of SVMs. Instead, the proposed algorithm is applied during the test phase and works with existing schemes. The proposed algorithm modifies a kernel parameter in the decision function of SVMs to alter SVM decisions for better classification accuracy based on the previous outputs of SVMs. Since speech and music frames exhibit strong inter-frame correlation, the outputs of SVMs can guide the kernel parameter modification. Our experimental results show that the proposed algorithm has the potential for adaptively tuning classifications of support vector machines for better performance.
ER -
English
