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

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.

Distance Metric Learnt Kernel-Based Music Classification Using Timbral Descriptors

2021 ◽  
Author(s):  
Rajeev Rajan ◽  
B. S. Shajee Mohan
Keyword(s):  
Folk Music ◽  
Nearest Neighbor ◽  
Metric Learning ◽  
Support Vector ◽  
Distance Metric ◽  
K Nearest Neighbor ◽  
Genre Classification ◽  
Rbf Kernel ◽  
Music Genre ◽  
Music Genre Classification

Automatic music genre classification based on distance metric learning (DML) is proposed in this paper. Three types of timbral descriptors, namely, mel-frequency cepstral coefficient (MFCC) features, modified group delay features (MODGDF) and low-level timbral feature sets are combined at the feature level. We experimented with k nearest neighbor (kNN) and support vector machine (SVM)-based classifiers for standard and DML kernels (DMLK) using GTZAN and Folk music dataset. Standard kernel-based kNN and SVM-based classifiers report classification accuracy (in%) of 79.03 and 90.16, respectively, on GTZAN dataset and 86.60 and 92.26, respectively, for Folk music dataset, with the best performing RBF kernel. A further improvement was observed when DML kernels were used in place of standard kernels in the kernel kNN and SVM-based classifiers with an accuracy of 84.46%, 92.74% (GTZAN), 90.00 and 96.23 (Folk music dataset) for DMLK-kNN and DMLK-SVM, respectively. The results demonstrate the potential of DML kernels in music genre classification task.

scholarly journals Automatic Music Genre Classification and Its Relation with Music Education

2021 ◽  
Vol 11 (2) ◽  
pp. 36
Author(s):  
Hasan Can Ceylan ◽  
Naciye Hardalaç ◽  
Ali Can Kara ◽  
Fırat Hardalaç
Keyword(s):  
Music Education ◽  
Learning Algorithm ◽  
Mel Frequency Cepstral Coefficients ◽  
Music Production ◽  
Music Classification ◽  
Music Genres ◽  
Deep Learning Algorithm ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

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.

scholarly journals Using Machine Learning to Classify Music Genre

2021 ◽  
Vol 9 (10) ◽  
pp. 39-44
Author(s):  
Rachaell Nihalaani
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Art Form ◽  
Genre Classification ◽  
Machine Learning Model ◽  
Nearest Neighbours ◽  
Music Genre ◽  
The Mind ◽  
Music Genre Classification

Abstract: As Plato once rightfully said, ‘Music gives a soul to the universe, wings to the mind, flight to the imagination and life to everything.’ Music has always been an important art form, and more so in today’s science-driven world. Music genre classification paves the way for other applications such as music recommender models. Several approaches could be used to classify music genres. In this literature, we aimed to build a machine learning model to classify the genre of an input audio file using 8 machine learning algorithms and determine which algorithm is the best suitable for genre classification. We have obtained an accuracy of 91% using the XGBoost algorithm. Keywords: Machine Learning, Music Genre Classification, Decision Trees, K Nearest Neighbours, Logistic regression, Naïve Bayes, Neural Networks, Random Forest, Support Vector Machine, XGBoost

Classification of Music Genres Based on Music Separation into Harmonic and Drum Components

2015 ◽  
Vol 39 (4) ◽  
pp. 629-638 ◽  
Author(s):  
Aldona Rosner ◽  
Bjӧrn Schuller ◽  
Bozena Kostek
Keyword(s):  
Support Vector Machine ◽  
Audio Signal ◽  
Signal Separation ◽  
Support Vector ◽  
Svm Classifier ◽  
Training Method ◽  
Feature Vectors ◽  
Genre Classification ◽  
Music Genre Classification

Abstract This article presents a study on music genre classification based on music separation into harmonic and drum components. For this purpose, audio signal separation is executed to extend the overall vector of parameters by new descriptors extracted from harmonic and/or drum music content. The study is performed using the ISMIS database of music files represented by vectors of parameters containing music features. The Support Vector Machine (SVM) classifier and co-training method adapted for the standard SVM are involved in genre classification. Also, some additional experiments are performed using reduced feature vectors, which improved the overall result. Finally, results and conclusions drawn from the study are presented, and suggestions for further work are outlined.

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