Open AccessSOME NEW RESULTS ON AUTOMATIC IDENTIFICATION OF VIETNAMESE FOLK SONGS CHEO AND QUANHO
Author(s) -
Chu Bá Thành,
Trịnh Văn Loan,
Nguyễn Hồng Quang
Publication year - 2020
Publication title -
journal of computer science and cybernetics (vietnam academy of science and technology)/journal of computer science and cybernetics
Language(s) - English
Resource type - Journals
eISSN - 2815-5939
pISSN - 1813-9663
DOI - 10.15625/1813-9663/36/4/14424
Subject(s) - vietnamese , mel frequency cepstrum , speech recognition , identification (biology) , computer science , energy (signal processing) , mixture model , folk song , gaussian , pattern recognition (psychology) , artificial intelligence , mathematics , linguistics , feature extraction , art , literature , statistics , chemistry , philosophy , biology , botany , computational chemistry
Vietnamese folk songs are very rich in genre and content. Identifying Vietnamese folk tunes will contribute to the storage and search for information about these tunes automatically. The paper will present an overview of the classification of music genres that have been performed in Vietnam and abroad. For two types of very popular folk songs of Vietnam such as Cheo and Quan ho, the paper describes the dataset and GMM (Gaussian Mixture Model) to perform the experiments on identifying some of these folk songs. The GMM used for experiment with 4 sets of parameters containing MFCC (Mel Frequency Cepstral Coefficients), energy, first derivative and second derivative of MFCC and energy, tempo, intensity, and fundamental frequency. The results showed that the parameters added to the MFCCs contributed significantly to the improvement of the identification accuracy with the appropriate values of Gaussian component number M. Our experiments also showed that, on average, the length of the excerpts was only 29.63% of the whole song for Cheo and 38.1% of the whole song for Quan ho, the identification rate was only 3.1% and 2.33% less than the whole song for Cheo and Quan ho respectively.