COMPARISON OF K-NN AND SVM CLASSIFIER FOR MUSIC GENRE CLASSIFICATION

Abstract: Recommendation of music is one of the predominant things, like streaming platforms of music. Music genres are the frames used to catalogue music files. Most of the music classification is initiated by the extraction of the audio features which calls for computing processes. This scrutiny aims the analysis and tests the performance of the classification of music genre based on the functionality of two different classifiers, such as Support Vector Machine (SVM) and K Nearest Neighbors (K-NN). The music dataset of Spotify was chosen as it had the functionality of each of its musical genres. The results correspond to the audio feature extraction, hence the classification with the extortion of functionality features can be developed more if the functionality in the dataset is managed well.

Keywords: Music Genre, K-NN, Support Vector Machine, Audio Features