In this study, a discriminative weight training is applied to a support vector machine (SVM) based speech/music classification for a 3GPP2 selectable mode vocoder (SMV). In the proposed approach, the speech/music decision rule is derived by the SVM by incorporating optimally weighted features derived from the SMV based on a minimum classification error (MCE) method. This method differs from that of the previous work in that different weights are assigned to each feature of the SMV a novel process. According to the experimental results, the proposed approach is effective for speech/music classification using the SVM.
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Sang-Kyun KIM, Joon-Hyuk CHANG, "Discriminative Weight Training for Support Vector Machine-Based Speech/Music Classification in 3GPP2 SMV Codec" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 1, pp. 316-319, January 2010, doi: 10.1587/transfun.E93.A.316.
Abstract: In this study, a discriminative weight training is applied to a support vector machine (SVM) based speech/music classification for a 3GPP2 selectable mode vocoder (SMV). In the proposed approach, the speech/music decision rule is derived by the SVM by incorporating optimally weighted features derived from the SMV based on a minimum classification error (MCE) method. This method differs from that of the previous work in that different weights are assigned to each feature of the SMV a novel process. According to the experimental results, the proposed approach is effective for speech/music classification using the SVM.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.316/_p
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@ARTICLE{e93-a_1_316,
author={Sang-Kyun KIM, Joon-Hyuk CHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Discriminative Weight Training for Support Vector Machine-Based Speech/Music Classification in 3GPP2 SMV Codec},
year={2010},
volume={E93-A},
number={1},
pages={316-319},
abstract={In this study, a discriminative weight training is applied to a support vector machine (SVM) based speech/music classification for a 3GPP2 selectable mode vocoder (SMV). In the proposed approach, the speech/music decision rule is derived by the SVM by incorporating optimally weighted features derived from the SMV based on a minimum classification error (MCE) method. This method differs from that of the previous work in that different weights are assigned to each feature of the SMV a novel process. According to the experimental results, the proposed approach is effective for speech/music classification using the SVM.},
keywords={},
doi={10.1587/transfun.E93.A.316},
ISSN={1745-1337},
month={January},}
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TY - JOUR
TI - Discriminative Weight Training for Support Vector Machine-Based Speech/Music Classification in 3GPP2 SMV Codec
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 316
EP - 319
AU - Sang-Kyun KIM
AU - Joon-Hyuk CHANG
PY - 2010
DO - 10.1587/transfun.E93.A.316
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2010
AB - In this study, a discriminative weight training is applied to a support vector machine (SVM) based speech/music classification for a 3GPP2 selectable mode vocoder (SMV). In the proposed approach, the speech/music decision rule is derived by the SVM by incorporating optimally weighted features derived from the SMV based on a minimum classification error (MCE) method. This method differs from that of the previous work in that different weights are assigned to each feature of the SMV a novel process. According to the experimental results, the proposed approach is effective for speech/music classification using the SVM.
ER -