Before explaining the project, here are the description of each file. Each file can be compiled independently of the others.
| Python files | Description |
|---|---|
00_Preprocess_Music.py |
Process of the audio dataset to extract features and save them in CSV files |
01_NeuralNetworks_Hyperparam.py |
Comparison of Neural Networks architectures and models, Hyperparametrization |
02_NN_Train_Model.py |
Train, save and export the chosen model |
03_Launch_Classifier.py |
Prediction of the musical genre independently using the trained model |
FunctionsDataViz.py |
Functions to do some data visualization |
FunctionsNN.py |
Functions for the Neural Networks |
FunctionsInterface.py |
Functions for the prediction of a user-chosen music (scrapping and prediction) |
Music_Genre_Classifier_launcher.ipynb |
Notebook jupyter to launch the interface |
| Input files | Description |
|---|---|
{}.csv |
All the CSV files with extracted features |
trained model |
Keras folder with the trained model |
classes_ordered.txt |
The order of the classes to know the right index of prediction |
- Basics :
numpy(1.19.2),pandas(1.2.1) - Visualization :
seaborn(0.11.1),matplotlib(3.3.2),IPython(7.20.0) - Music processing and analysis :
librosa(0.8.0) - Scrapping :
requests(2.25.1),urllib(1.26.3),re(2020.6.8) - Media :
ffmepg(3.2.4),youtubedl(2021.02.04.1) - Model :
tensorflow(2.3.0),sklearn(0.23.2)
Click here to go to our Wiki page
Lansdown, Bryn. (2019). Machine Learning for Music Genre Classification.
Panagakis, Yannis & Kotropoulos, C.. (2010). Music genre classification via Topology Preserving Non-Negative Tensor Factorization and sparse representations. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 249 - 252.
Tzanetakis, George & Cook, Perry. (2002). Musical Genre Classification of Audio Signals. IEEE Transactions on Speech and Audio Processing. 10. 293-302.