Automatic Music Genres Classification using Machine Learning

Abstract

Classification of music genre has been an inspiring job in the area of music information retrieval (MIR). Classification of genre can be valuable to explain some actual interesting problems such as creating song references, finding related songs, finding societies who will like that specific song. The purpose of our research is to find best machine learning algorithm that predict the genre of songs using k-nearest neighbor (k-NN) and Support Vector Machine (SVM). This paper also presents comparative analysis between k-nearest neighbor (k-NN) and Support Vector Machine (SVM) with dimensionality return and then without dimensionality reduction via principal component analysis (PCA). The Mel Frequency Cepstral Coefficients (MFCC) is used to extract information for the data set. In addition, the MFCC features are used for individual tracks. From results we found that without the dimensionality reduction both k-nearest neighbor and Support Vector Machine (SVM) gave more accurate results compare to the results with dimensionality reduction. Overall the Support Vector Machine (SVM) is much more effective classifier for classification of music genre. It gave an overall accuracy of 77%.

Authors and Affiliations

Muhammad Asim Ali, Zain Ahmed Siddiqui

Keywords

Related Articles

FPGA Implementation of RISC-based Memory-centric Processor Architecture

The development of the microprocessor industry in terms of speed, area, and multi-processing has resulted with increased data traffic between the processor and the memory in a classical processor-centric Von Neumann comp...

A hybrid Evolutionary Functional Link Artificial Neural Network for Data mining and Classification

This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification tasks of data mining. In fact, there are a few stud...

Constraints in the IoT: The World in 2020 and Beyond

The Internet of Things (IoT), often referred as the future Internet; is a collection of interconnected devices integrated into the world-wide network that covers almost everything and could be available anywhere. IoT is...

Probabilistic Monte-Carlo Method for Modelling and Prediction of Electronics Component Life

Power electronics are widely used in electric vehicles, railway locomotive and new generation aircrafts. Reliability of these components directly affect the reliability and performance of these vehicular platforms. In re...

Performance Evaluation of Content Based Image Retrieval on Feature Optimization and Selection Using Swarm Intelligence

The diversity and applicability of swarm intelligence is increasing everyday in the fields of science and engineering. Swarm intelligence gives the features of the dynamic features optimization concept. We have used swar...

Download PDF file
  • EP ID EP260477
  • DOI 10.14569/IJACSA.2017.080844
  • Views 122
  • Downloads 0

How To Cite

Muhammad Asim Ali, Zain Ahmed Siddiqui (2017). Automatic Music Genres Classification using Machine Learning. International Journal of Advanced Computer Science & Applications, 8(8), 337-344. https://europub.co.uk/articles/-A-260477