Drum Sound Classification

A python module for making pandas datasets out of drum libraries, and training drum type classification models using a few different methods. Read my accompanying blog post.

Set up

1. Install requirements with pip. From this cloned repository it should be enough to do pip install ., but using a virtual environment is encouraged.
2. Get drum sounds. I recommend r/drumkits
3. Run something like python drum_sound_classifier/preprocess.py --drum_lib_path /path/to/drums. This will recursively search, so nested directories are fine. It will safely skip any non-audio files. Run python preprocess.py --help for options.
   • If you would like to inspect the resulting dataset yourself, this will create a pickled pandas dataframe data/interim/dataset.pkl

Training

To train a random forrest classifier on drum descriptors, run:

python drum_sound_classifier/models/train_sklearn.py --inputs descriptors --model random_forest

You may want to add --no_extract_spectrograms if you don't plan on using a GPU to train a CNN model. Otherwise, spectrogram data will be pre-extracted which takes up disk space.

To train a CNN-based model (a GPU is essential), run:

python drum_sound_classifier/models/train_cnn.py

And finally, assuming you have a CNN model trained, to try a SVC over CNN embeddings run:

python drum_sound_classifier/models/train_sklearn.py --inputs cnn_embeddings --model svc

Run any of the above with --help for options.

Inference

I have yet to add support for inference using sklearn-derived models (pull requests welcome!), but to infer with CNN models see inference.py