Review on Music Classification using Machine Learning

Review on Music Classification using Machine Learning

Asmita Patil, Pallavi Wankhede, Aditi Nimbalkar, Jyoti Y. Deshmukh

ABSTRACT -

Music is the most popular area, particularly among all people. Most individuals like to listen to music in a specific genre, such as classical, hip-hop, or disco, and they desire an easy way to categorise the music according to their mood. Due to the selection and extraction of relevant data, music genre categorization in music information retrieval (MIR) is a challenging process. The methodology for classifying songs and audio music into the appropriate genres is known as the Music Genre Classification Model(MGCM). A more effective and precise model for this classification has to be created since people's lifestyles are growing more reliant on music, technology and the internet as these things become more affordable to end users. We are using deep learning in the suggested system. Convolution neural networks (CNN) are used to categorise music into different genres. Acoustic feature extraction is the most important process while evaluating music. The model in the proposed system is trained using the GTZAN dataset.

Keywords: Deep Learning, Classification, Convolution Neural Network, Music Information Retrieval, Feature Extraction.