This projects uses Convolutional Neural Networks to classify music from the GTZAN dataset.
The blog post is available here: https://egarciamartin.github.io/2021/music-cnn/, where I explain the problem, solutions, and all details about the models used in the cnn.py and CNN.ipynb files.
- code:
- python script:
cnn.py - jupyter notebook:
CNN.ipynb
- python script:
- models: pre-trained models
- png: figures for the blog post
- blog: blog md and pdf
- blog.pdf generated from blog.md with the following command:
pandoc blog.md --pdf-engine=xelatex -o blog.pdf
- For help on usage:
python cnn.py -h - Example to train:
python cnn.py train ../data/genres -v -m lenet - Example to predict:
python cnn.py predict ../data/genres/ -f ../data/genres/rock/rock.00039.wav -m cnn64 -vThe script can either train a new CNN from scratch (either a LeNet or a CNN64, for more information please check the blog), or predict what is the genre using a pre-trained model given the path to a wav song together with the path to the labels (folders).
The data is obtained from: Download link and expected to be saved in a directory called data at the same level as the code and models folders.
- CNN64: Accuracy: 87% on all training validation and test sets using the CNN64 architecture. Took around 10 hours to run
- LeNet: Accuracy on the training, validations and tests sets, respectively: 94%, 76%, 78%