Music Genre Classification using Machine Learning
Anirudh Ghildiyal, Komal Singh, Sachin Sharma
Abstract
The music industry has undergone major changes from its conventional existence and also in the form of music created in last few years. The ever-growing customer base has also increased the market for different music styles. Music not only bring the individuals together, but also provides insight for various cultures. Therefore, it is essential to classify the music according to the genres to satisfy the needs of the people categorically. The manual ranking of music is a repetitive, lengthy task and the duty lies with the listener. The proposed research work has compared few classification models and established a new model for CNN, which is better than previously proposed models. This research work has trained and compared the proposed models on GTZAN dataset, where most of the models were audio file trains, while a few of the models were trained on the spectrogram.