In this music genre classification project, a classifier on audio files to predict its genre is developed.
This project uses the GTZAN music genre classification dataset.
The GTZAN genre collection dataset was collected in 2000-2001. It consists of 1000 audio files each having 30 seconds duration. There are 10 classes ( 10 music genres) each containing 100 audio tracks. Each track is in .wav format. It contains audio files of the following 10 genres:
- Blues
- Classical
- Country
- Disco
- Hiphop
- Jazz
- Metal
- Pop
- Reggae
- Rock
There are various methods to perform classification on this dataset. Some of these approaches are:
- Multiclass support vector machines
- K-means clustering
- K-nearest neighbors
- Convolutional neural networks
This project uses K-nearest neighbors algorithm because in various researches it has shown better results for this problem.