Music Genre Classification Using Machine Learning

  M.D.Nevetha,  A.Nithyasree,  A.Parveenbanu,  Mrs.Jetlin CP

Machine Learning is an application of Artificial Intelligence(AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In this paper, we've got put forth a expressive style classification approach using Machine Learning technique. Music plays a really important role in people’s lives. Music brings like-minded people together and is that the glue that holds communities together. Communities may be recognized by the kind of songs that they compose, or maybe hear. within the area of Music Information Retrieval (MIR), categorizing musical genre could be a challenging task. the aim of our project and research is to seek out a far better machine learning algorithm than the pre-existing models that predicts the genre of songs. Genres may be defined as categorical labels created by humans to spot or characterize the design of music. The concept of automatic expressive style classification has become very talked-about in recent years as a results of the rising of the digital show business. This work presents a comprehensive machine learning approach to the matter of automatic style classification using the audio signal. The system is developed employing a Convolutional Neural Network (CNN) to acknowledge the genres. Here CNN model is trained end to end, to predict the genre label of an audio signal. We are conducting the experiment on the GTZAN data-set, which is widely used public data-set for research in music recognition (MGR).

Keywords- Convolutional Neural Network(CNN) , GTZAN data-set , Music Information Retrival (MIR) , Machine Learning(ML).

Unique Identification Number - IJEDR2102024
Page Number(s) - 155-159
Pubished in - Volume 9 | Issue 2 | May 2021
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  M.D.Nevetha,  A.Nithyasree,  A.Parveenbanu,  Mrs.Jetlin CP,   "Music Genre Classification Using Machine Learning", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.9, Issue 2, pp.155-159, May 2021, Available at :http://www.ijedr.org/papers/IJEDR2102024.pdf