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

scholarly journals Music Genre Classification using Optimized Sequential Neural Network

2021 ◽  
Vol 10 (3) ◽  
pp. 1949-1958
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Human Beings ◽  
Advantages And Disadvantages ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

Music makes up a huge portion of the contents stored and used over the internet, with several sites and applications developed solely to provide music-related services to their users/ customers.Some of the most challenging tasks in this scenario would include music classification based on languages and genres, playlist suggestions based on music history, song suggestions based on playlist contents, top genres / songs based on listeners' rating, likes, number of streams, song loops, popularity of artists based on number of songs released per year, hit songs per year, etc. One of the most important stages to solve the above-mentioned challenges would be music genre classification. It would be impractical to analyze each and every song in a given database to identify and classify music genres, even though human beings are better at performing such tasks. Hence, useful Machine Learning algorithms and Deep Learning approaches may be used for accomplishing such tasks with ease. A thorough analysis to understand the different uses of Machine Learning and Deep Learning algorithms and relevance of such algorithms with respect to situations would be made to highlight and contrast the advantages and disadvantages of each approach. The outcomes of the optimized models would be visualized and comparedto the expected outcomes for better perception.

Automatic Music Genre Classification Based on Linguistic Frequencies Using Machine Learning

2021 ◽  
Author(s):  
Mekala Srinivasa Rao ◽  
O. Pavan Kalyan ◽  
N. Naresh Kumar ◽  
Md. Tasleem Tabassum ◽  
B. Srihari
Keyword(s):  
Machine Learning ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

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.

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