NDLI: Audio Signal Classification Based on Optimal Wavelet and Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Shantha Selva Kumari, R. Sugumar, D. Sadasivam, V.
Copyright Year	2007
Description	Author affiliation: Mepco Schlenk Eng. Coll., Sivakasi (Shantha Selva Kumari, R.; Sugumar, D.)
Abstract	Audio signal classification is done by extracting relevant features from a sound and using these extracted features, it identifies into which of a set of classes the sound is most likely to fit. In this paper, an improved feature vector formation technique for audio classification and categorization is presented. This technique makes use of wavelets to extract the features of audio data. Wavelets are first applied to decompose the signal and to extract acoustical features such as sub-band power, brightness, band width and pitch information. The additional features, such as frequency cepstral coefficients also extracted to accomplish audio classification. This paper also proposes a different frame size computation strategy that uses 512 samples per frame. The overlap size between windowed frames of the proposed feature extraction algorithm is redesigned and a vector formation process is carried out after feature extraction. Finally, the proposed method uses a bottom-up support vector machine over these acoustical features and additional features. The bottom-up support vector machine categorization strategy uses an iterative procedure to match a given audio to progressively larger subsets or categories of classes. In addition, two support vector machine training parameters such as the upper bound and the variance of the exponential radial basis function are modified to improve the accuracy of classification. It is shown in experimental results that the categorization accuracy of a given audio sound can achieve 100% in the top 1 and top 2 levels.
Starting Page	544
Ending Page	548
File Size	329394
Page Count	5
File Format	PDF
ISBN	0769530508
DOI	10.1109/ICCIMA.2007.370
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-13
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern classification Support vector machines Support vector machine classification Feature extraction Data mining Brightness Frequency Cepstral analysis Iterative algorithms Upper bound
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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