Improvement of SVM-Based Speech/Music Classification Using Adaptive Kernel Techniqueopen access
- Authors
- Lim, Chungsoo; Chang, Joon-Hyuk
- Issue Date
- Mar-2012
- Publisher
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- Keywords
- SVM; SMV; adaptive kernel; sigmoid
- Citation
- IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E95D, no.3, pp.888 - 891
- Indexed
- SCOPUS
- Journal Title
- IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
- Volume
- E95D
- Number
- 3
- Start Page
- 888
- End Page
- 891
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/27585
- DOI
- 10.1587/transinf.E95.D.888
- ISSN
- 1745-1361
- 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.
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Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
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