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
- 차경준
- 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%2Fs12206-018-0502-4https://repository.hanyang.ac.kr/handle/20.500.11754/119140
- ISSN
- 1738-494X; 1976-3824
- DOI
- 10.1007/s12206-018-0502-4
- Appears in Collections:
- COLLEGE OF NATURAL SCIENCES[S](자연과학대학) > MATHEMATICS(수학과) > Articles
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