• Niharika Deepti
• Nidhi Verma
• Mr. Mahendra Kumar

Today’s music is very important part of people’s daily lives. There are various genre of music and because the genres are different, the taste of music is also different. This increases the need for proper cataloging and greater reach. Radio stations and music TV channels have archives of millions of music tapes. Gigabytes of music files are also spread across the web. Automating the search and organization of music files by their genre is a daunting task. In this paper, we describe an automatic classification system model for musical genres. Our research objective is to find the best machine learning algorithm that predicts genre using K-Nearest Neighbor machine (KNN) and Support Vector Machine (SVM). In this we present the GTZAN music dataset. It has ten different genres of music.

Music, genre, classification, features, Mel Frequency Cepstral Coefficients (MFCC), K- Nearest Neighbor (KNN), Support Vector Machine (SVM), Librossa, GTZAN Dataset.

"Music Genre Classification ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.b568-b574, June-2022, Available :http://www.jetir.org/papers/JETIR2206176.pdf

"Music Genre Classification ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppb568-b574, June-2022, Available at : http://www.jetir.org/papers/JETIR2206176.pdf