Automatic Music Genre Classification and Its Relation with Music  Education

Because the classification saves time in the learning process and enables this process to take place more easily, its contribution to music learning cannot be denied. One of the most valid and effective methods in music classification is music genre classification. Given the rapid progress of music production in the world and the significant increase in the number of data, the process of classifying music genres has now become too complex to be done by humans. Considering the successful results of deep neural networks in this field, the aim is to develop a deep learning algorithm that can classify 10 different music genres. To reveal the efficiency of the model by comparing it with others, we make the classification using the GTZAN dataset, which was previously used in many studies and retains its validity. In this article, we use a convolutional neural network (CNN) to classify music genres, taking into account the previous successful results. Unlike previous studies in which CNN was used as a classifier, we represent music segments in the dataset by mel frequency cepstral coefficients (MFCC) instead of using visual features or representations. We obtain MFCCs by preprocessing the music pieces in the dataset, then train a CNN model with the acquired MFCCs and determine the success of the model with the testing data. As a result of this study, we develop a model that is successful in classifying music genres by using smaller data than previous studies.

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Music Genre Classification using Deep Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36087 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 66-71

Author(s):

Sheeba Fathima

Keyword(s):

Neural Networks ◽

Machine Learning Techniques ◽

Data Set ◽

Music Production ◽

Genre Classification ◽

Learning Techniques ◽

Network Topologies ◽

Classification Tasks ◽

Music Genre ◽

Music Genre Classification

Many subjects are affected by digital music production., including music genre prediction. Machine learning techniques were used to classify music genres in this research. Deep neural networks (DNN) have recently been demonstrated to be effective in a variety of classification tasks. Including music genre classification. In this paper, we propose two methods for boosting music genre classification with convolutional neural networks: 1) using a process inspired by residual learning to combine peak- and average pooling to provide more statistical information to higher level neural networks; and 2) To bypass one or more layers, use shortcut connections. To perform classification, the KNN output is fed into another deep neural network. Our preliminary experimental results on the GTZAN data set show that the above two methods, especially the second one, can effectively improve classification accuracy when compared to two different network topologies.

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Music Genre Classification Using Deep Learning Techniques

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35965 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4157-4160

Author(s):

M.K.N. Haq

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Art Music ◽

Musical Form ◽

Musical Style ◽

Religious Music ◽

Music Genres ◽

Deep Learning Algorithm ◽

Music Genre ◽

Musical Genres

Music genre labels are useful to organize various songs, albums, and artists into broader groups that share related musical genres such as similar sound etc. A music genre is a conventional group it helps us to identify some pieces of music as belonging to a shared tradition or set of conventions. It is to be distinguished from musical form and musical style, although this terms can be used as viceversa. Music can be divided into genres in varying ways such as into popular music and art music,hip hop music or religious music and secular music. The artistic nature of music means that these classifications are often biased and notorious and some genres may overlap. We will classify the various music genres by using deep learning algorithm. We will train the model and by using various music genres of test dataset we will predict the specific music genre.

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Indonesian Music Classification on Folk and Dangdut Genre Based on Rolloff Spectral Feature Using Support Vector Machine (SVM) Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.54646 ◽

2021 ◽

Vol 15 (1) ◽

pp. 11

Author(s):

Brizky Ramadhani Ismanto ◽

Tubagus Maulana Kusuma ◽

Dina Anggraini

Keyword(s):

Support Vector Machine ◽

Folk Music ◽

Spectral Feature ◽

Folk Songs ◽

Support Vector ◽

Music Classification ◽

Genre Classification ◽

Svm Algorithm ◽

Music Genre ◽

Music Genre Classification

Music Genre Classification is one of the interesting digital music processing topics. Genre is a category of artistry, in this case, especially music, to characterize and categorize music is now available in various forms and sources. One of the applications is in determining the music genre classification on folk songs and dangdut songs.The main problem in the classification music genre is to find a combination of features and classifiers that can provide the best result in classifying music files into music genres. So we need to develop methods and algorithms that can classify genres appropriately. This problem can be solved by using the Support Vector Machine (SVM). The genre classification process begins by selecting the song file that will be classified by the genre, then the preprocessing process, the collection features by utilizing feature extraction, and the last process is Support Vector Machine (SVM) classification process to produce genre types from selected song files. The final result of this research is to classify Indonesian folk music genre and dangdut music genre along with the 83.3% accuracy values that indicate the level of system relevance to the results of music genre classification and to provide genre labels on music files as to facilitate the management and search of music files.

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