Music Genre Classification Using XGB Boost

It is a scientifically proven fact that music can heal a person. Today, around the world, many individuals are coping with their health issues using music therapies. Music is a part of our lives. Every occasion is covered by our songs, which are categorized by genre for simple access. Songs appeal to us for a variety of reasons, including their melody, words, pitch, feelings, and more. A genre is attributed to a song based on all these characteristics. So, to recommend music knowing which genre it belongs to is very essential. The authors created a system just right for that. Our system will help in classifying a song into various genres available. This was achieved by us using the XGBoost algorithm. Model construction and data preparation are the two procedures implemented. As a result of using the GTZAN (G. Tzanetakis and P. Cook) dataset, which comprises 1000 audio files in 10 genres, each lasting 30 s, has been implemented in our model. With the help of the LIBROSA package, we were able to extract the 14 features and use them to enhance the data when training our model for greater accuracy. As a result, we were able to reach 81% accuracy. Since it offers low data loss and is ideal for medium-sized data like ours, XGB boost has been chosen.