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Music genre classification with LSTM Recurrent Neural Nets in Keras & PyTorch
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README.md

Music Genre Classification with LSTMs

  • Classify music files based on genre from the GTZAN music corpus
  • GTZAN corpus is included for easy of use
  • Use multiple layers of LSTM Recurrent Neural Nets
  • Implementations in PyTorch, Keras & Darknet.

Test trained LSTM model

In the ./weights/ you can find trained model weights and model architecture.

To test the model on your custom audio file, run

 python3 predict_example.py path/to/custom/file.mp3

or to test the model on our custom files, run

 python3 predict_example.py audios/classical_music.mp3

Audio features extracted

Dependencies

  • Python3
  • numpy
  • librosa - for audio feature extraction
  • Keras
    • pip install keras
  • PyTorch
    • pip install torch torchvision
    • brew install libomp

Ideas for improving accuracy:

  • GTZAN dataset has problems, how do we use it with consideration?
  • Normalize MFCCs & other input features (Recurrent BatchNorm?)
  • Decay learning rate
  • How are we initing the weights?
  • Better optimization hyperparameters (too little dropout)
  • Do you have avoidable bias? How's your variance?

Accuracy

At Epoch 400, training on a TITAN X GPU (October 2017):

Loss Accuracy
Training 0.5801 0.7810
Validation 0.734523485104 0.766666688025
Testing 0.900845060746 0.683333342274

At Epoch 400, training on a 2018 Macbook Pro CPU (May 2019):

Loss Accuracy
Training 0.3486 0.8738
Validation 1.028421084086 0.700000017881
Testing 1.209656755129 0.683333347241
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