Multi Genre Music Classification and Conversion System

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Irfan Siddavatam 1,* Ashwini Dalvi 1 Dipen Gupta 1 Zaid Farooqui 1 Mihir Chouhan 1

1. Department of Information Technology, K.J.Somaiya College of Engineering, Mumbai-400077

* Corresponding author.


Received: 29 Aug. 2019 / Revised: 10 Sep. 2019 / Accepted: 25 Oct. 2019 / Published: 8 Feb. 2020

Index Terms

Music, Artificial Intelligence, Genre, Music Classification, Music Conversion, Convolution Neural Network (CNN)


Artificial Intelligence (AI) has a huge scope in automating, stream- lining, and increasing productivity of Music Industry. Here, we look upon AI based techniques for classifying a piece of music into multiple genres and then later converting it into another user-specified genre. Plenty of work has been done in classification, but using traditional machine learning models which are limited in term of accuracy and rely heavily on features to train the model. The novelty of this work lies in its attempt to covert genre of music from one type to another. This paper focuses on classification achieved by using a model trained via Convolutional Neural Networks. Conversion of music genre, a relatively less worked upon field has been discussed in this paper along with details of implementation. For Conversion, we initially convert the input file to spectrogram. A database of all genre is maintained at all times and a random file from user selected genre is also converted to spectrogram. Later, these spectrograms are processed and converted back to signals. Finally the user can listen to the converted audio file. Validation of the conversion was performed via a survey with the help of end users. Thus, a novel idea of doing Music Genre Conversion was put forth and was validated with positive outcomes.

Cite This Paper

Irfan Siddavatam, Ashwini Dalvi, Dipen Gupta, Zaid Farooqui, Mihir Chouhan, "Multi Genre Music Classification and Conversion System", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.12, No.1, pp. 30-36, 2020. DOI:10.5815/ijieeb.2020.01.04


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