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• W3018474250 abstract "With the fast development of computer and information technology, multimedia data has become the most important form of information media. Auditory information plays an important role in information location, this comes from the fact that it can be difficult to find useful information. Thus audio classification becomes more important in audio analysis as it prepares for content-based audio retrieval. There is quite a bit of research on the topic of audio classification methods, audio feature analysis, and extraction based on audio classification. Many works of literature extract features of audio signals based on time or Fourier transform frequency domain. The emergence of the wavelet theory provides a time-frequency analysis tool for signal analysis. Wavelet transformation is a local transformation of the signal in time and frequency which can effectively extract information from the signal, and perform multi-scale refinement analysis on functions or signals through operations such as stretching and translation instead of the traditional Fourier transformation. In the time-frequency analysis of the signal, the wavelet analysis captures the local time and frequency characters of the signal which can improve the ability of signal analysis. It can also change certain locals of the signal without affecting other aspects of it. In this paper, the frequency domain features are combined with the wavelet domain features. At the same time that the MFCC features are extracted, the discrete wavelet transform is used to extract the features of the wavelet domain. Then the statistical features are extracted for each audio example, and the SVM model is used to realize the different forms of audio classification identification." @default.
• W3018474250 created "2020-05-01" @default.
• W3018474250 creator A5076380704 @default.
• W3018474250 date "2020-01-01" @default.
• W3018474250 modified "2023-09-24" @default.
• W3018474250 title "Audio Example Recognition and Retrieval Based on Geometric Incremental Learning Support Vector Machine System" @default.
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• W3018474250 doi "https://doi.org/10.1109/access.2020.2988686" @default.
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