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Classification of acoustic noise signals using wavelet spectrum based support vector machine

Title
Classification of acoustic noise signals using wavelet spectrum based support vector machine
Author
배석주
Keywords
Air-conditioner; Refrigerant noises; Diagnosis; Hurst exponent; Regression; Support vector machine
Issue Date
2018-06
Publisher
KOREAN SOC MECHANICAL ENGINEERS
Citation
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v. 32, no. 6, page. 2453-2462
Abstract
Harsh noises come from air-conditioning units are chronic complaining issues to their users. Individual perceptions of noise levels have been generally quantified by means of subjective evaluation such as a jury test. This article proposes a classification approach to acoustic noise signals using a wavelet spectrum analysis. We derive energy spectrums of noise signals using a discrete wavelet transform at pre-specified window length. The energy spectrums are a linear form and represented by a Hurst parameter as an informative summary of long-range dependent signal data. The Hurst parameter controls the self-similarity scaling as well as the degree of long-range dependence. We estimate the Hurst parameter through the least squares regression of sample energy against a resolution level in the wavelet spectral domain. In the context of multi-class classification problem, the classification of noise signals is performed by a nonlinear support vector machine (SVM) for parameter estimates of linear energy profiles containing the Hurst parameter. In an application example of air-conditioner noise signals, empirical results show that the proposed method offers the higher level of accuracy in acoustic noise sound classification.
URI
https://link.springer.com/article/10.1007%2Fs12206-018-0502-4http://repository.hanyang.ac.kr/handle/20.500.11754/118890
ISSN
1738-494X; 1976-3824
DOI
10.1007/s12206-018-0502-4
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INDUSTRIAL ENGINEERING(산업공학과) > Articles
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