4107_Final_Project
Thanks to:
- https://gitlab.crowdai.org/gg12/WWWMusicalGenreRecognitionChallenge?_ga=2.123714251.1594486358.1545011930-1055586743.1544557330
- https://hackernoon.com/finding-the-genre-of-a-song-with-deep-learning-da8f59a61194
- https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb
- https://ricardodeazambuja.com/deep_learning/2018/05/04/tuning_tensorflow_docker/
- https://medium.com/@moritzkrger/speeding-up-keras-with-tfrecord-datasets-5464f9836c36
- https://github.com/markjay4k/Fashion-MNIST-with-Keras/blob/master/pt3%20-%20FMINST%20Embeddings.ipynb
- https://github.com/keras-team/keras/blob/master/examples/conv_filter_visualization.py
Requirements:
Seen in requirements.txt
| Library | Version |
|---|---|
| pandas | 0.23.4 |
| matplotlib | 3.0.2 |
| numpy | 1.15.4 |
| tensorflow | 1.12.0 |
| scipy | 1.1.0 |
| librosa | 0.6.2 |
| ipython | 7.2.0 |
| keras | 2.2.4 |
| scikit_learn | 0.20.1 |
Download Tracks
Either download your own music from http://www.ektoplazm.com/,
or
download music tracks from data_urls.txt
Convert Tracks to TFRecords
Detailed in read_mp3s.ipynb
Train model
Detailed in train_model.ipynb.
You can skip this step, and just extract best_model.tar.gz: a Keras model
I've trained.
Load this model using load_model(best_psy_cnn.h5)
Test model
Detailed in test_model.ipynb.
You can skip this step, and just extract best_model.tar.gz: a Keras model
I've trained.
Predict using the model
Detailed in predict_model.ipynb.
The model was trained on 5 genres:
Download your own psytrance tracks (that can be characterized as one of the above genres) and see how the model does!
To do this, make sure to compute the mel-spectrogram of your track, and feed the model with slices of your spectrogram.
Visualize Convolutional Filters
Detailed in visualize_filters.ipynb and
examples in ./tex/diagrams/stitched_filters
Report
The report outlining the development of this project is here
Production of graphs for the report are outlined in plot_results.ipynb