Implementation of Music Genre Classifier Using KNN Algorithm

Abstract

As music history grew, music began to diversify into different genres. Thisstudy aims to implement a music genre classifier using the KNN algorithm and a faster method. The KNN algorithm is accurate but with long execution time. This study implements a new method that can speed up the process of the KNN algorithm, and the K-means clustering algorithm inspires the idea. The dataset is preprocessed using the new idea. The program will select the song that is the centroid of the genre and use the method of the KNN to return the closest genre based on the distance from the test sample to the centroid. In conclusion, the new method did not perform well in accuracy but sped up the program. This study provides a great reference for the music genre classification problem in the machine learning domain. The study investigates an infeasible method in preprocessing data for the KNN algorithm optimization.