Musical Genre Classification

Three Models for Musical Genre Classification are discussed, one CNN model and two CRNN models. This project is based on Pytorch. Project report here.

Get Start:

Audio Files

• We use enhanced GTZAN Dataset, which contains 72 full songs and 1000 30s audio tracks.
• There are 10 genres, they are:

{0: 'pop',
 1: 'metal',
 2: 'disco',
 3: 'blues',
 4: 'reggae',
 5: 'classical',
 6: 'rock',
 7: 'hiphop',
 8: 'country',
 9: 'jazz'}

Log Mel-Spectrogram Datasets

We offers three pre-processed datasets, you can also generate datasets using Build Dataset Handmade.ipynb or Build Dataset.ipynb. Download Here

• Pure GTZAN Dataset (128^2 Chunks, 7000 in total)
• Mixed DatasetI (128^2 Chunks, 12370 in total)
• Mixed DatasetII (256^2 Chunks, 4533 in total)

Training

• Define Parameters in Paras.py
• Use train.py for training
• Training Logs saved in log fold (loss/accuracy vs epoch on train set and validation set)

Test

• Use music_dealer.py to predict the genre components of full song, see genre_predictor.ipynb and music_dealer.py for details
• Test result saved in log fold

Result

Test on frames

• Accuracy

• Confusion Matrix

Test on full songs

30 songs are used for test, Samples:

Thanks:

• https://www.mp3juices.cc/ for music download
• https://github.com/cetinsamet/music-genre-classification