• D. Bharadwaja
• T. Indu Priya
• N. Vishnu Priya
• M. Srirama Chandra
• T. Lohith

A music genre is a term used to group distinct musical styles together based on a custom or set of guidelines. It needs to be separated from musical form and style. There are many different ways to divide music into different genres. Pop, Hip-Hop, Rock, Jazz, Blues, Country, and Metal are the most popular musical genres. In the field of music information retrieval (MIR), classifying music files by genre is a difficult task. To retrieve music from a vast collection, automatic music genre classification is crucial. It has real-world applications in a variety of domains, such as automatic tagging of unknown piece of music (useful for apps like Gaana, Spotify, Saavn, Wynk etc.). This project aims to predict the genre using KNN algorithm with an audio signal as its input. Making the choosing of songs quick and less laborious is the goal of automating the music classification. If one has to manually classify the songs or music, one has to listen to a whole lot of songs and then select the genre. We are doing the experiment using the GTZAN data set, a popular public data set for music recognition research. (MGR).

Genres, Audio files, MFCC, KNN Algorithm

"Music Genre Classification Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.f676-f679, April-2023, Available :http://www.jetir.org/papers/JETIR2304983.pdf

"Music Genre Classification Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppf676-f679, April-2023, Available at : http://www.jetir.org/papers/JETIR2304983.pdf