An automatic classification system of the music genres is proposed. Based on the timbre features such as melfrequency cepstral coefficients, the spectro-temporal features are obtained to capture the temporal evolution and variation of the spectral characteristics of the music signal. Mean, variance, minimum, and maximum values of the timbre features are calculated. Modulation spectral flatness, crest, contrast, and valley are estimated for both original spectra and timbre-feature vectors. A support vector machine (SVM) is used as a classifier where an elaborated kernel function is defined. To reduce the computational complexity, an SVM ranker is applied for feature selection. Compared with the best algorithms submitted to the music information retrieval evaluation exchange (MIREX) contests, the proposed method provides higher accuracy at a lower feature dimension for the GTZAN and ISMIR2004 databases 1 .
PerspectiveIn September 2018, heat waves were declared to be a type of natural disaster by the Framework Act on the Management of Disasters and Safety. The present study examined the characteristics of heat waves from the perspectives of meteorological phenomena and health damage. The government's efforts to minimize the damages incurred by heat waves are summarized chronologically. Furthermore, various issues pertaining to heat waves that are being raised in our society despite the government's efforts are summarized by analyzing big data derived from reported news and academic articles. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/bync/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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