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This project can automatically classify three different musical genres (i.e., classical, metal, and reggae) from device-playing music files.

Music can be classified into genres in a variety of ways, including rock, pop, religious, and secular music. This project uses a deep learning approach to automatically classify different musical genres.

We’ll need a set of audio tracks that are similar in size and frequency range. The GTZAN genre classification dataset is the most widely recommended dataset for music genre classification projects, and it was collected specifically for this purpose. In the years 2000-2001, the GTZAN genre collection dataset was compiled. It is made up of 1000 audio files, each lasting 30 seconds. There are ten classes (10 music genres) with 100 audio tracks each. The tracks are all in.wav format. It includes audio tracks from the ten genres listed below:

- Blues
- Classical
- Country
- Disco
- Hiphop
- Jazz
- Metal
- Pop
- Reggae
- Rock”

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