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Classification Of Music Genre Using Machine Learning

Sravya Dukkupati, Sanka Saraswathi Datta, Vangaveti Nandini, Dr. S. Ramani


Machine Learning is a way  that helps the systems to automatically learn from experience and improve the performance and predict the outcome more accurately without any requirement of being explicitly programmed. Music is one of the most significant and influential part of people’s life. Also, music is known as a universal language as it has the power to unite people from different places and cultures. This helps in the recognition of various communities and their cultures by the type of songs they compose. The aim of this work is to identify the genre of a song using a higher machine learning formula than the pre-existing ones. Genres are human-created category labels for examining or displaying music styles. With the growth in digital exhibition industry, the concept of autonomous trend division has also grown a lot in popularity in the recent years. In the case of automatic genre separation using an audio signal, this work introduces a full machine learning framework. To identify the genre, the system makes use of a Convolutional Neural Network (CNN). The CNN model is trained from end - to - end to predict the type of audio signal. The GTZAN data set is used here, which is a widely known set used for music recognition (MGR) analysis. 

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