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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.

Automatic identification of music genre is of great importance in automating the process of storing, organizing and searching for vast amounts of information about music. Vietnam has folk songs that are very rich for all three regions of North -Central -South, including Quan ho Bac Ninh folk songs. This paper presents a method for identifying some of Quan ho Bac Ninh folk songs using the Gaussian Mixture Model (GMM) with model parameters including Mel Frequency Cepstral Coefficients (MFCCs), energy and fundamental frequency F0. The experiment results showed that the exact identification scores depend on the number of mixture components. The use of fundamental frequency information increased considerably the exact indentification score.

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We can say that music in general is an indispensable spiritual food in human life. For Vietnamese people, folk music plays a very important role, it has entered the minds of every Vietnamese person right from the moment of birth through lullabies for children. In Vietnam, there are many different types of folk songs that everyone loves, and each has many different melodies. In order to archive and search music works with a very large quantity, including folk songs, it is necessary to automatically classify and identify those works. This paper presents the method of determining the feature parameters and then using the convolution neural network (CNN) to classify and identify some Vietnamese folk tunes as Quanho and Cheo. Our experimental results show that the average highest classification and identification accuracy are 99.92% and 97.67%, respectivel.