Additional material for MSc dissertation "Deep Feature Extraction and Music Language Modelling for Amateur Vocal Percussion Transcription"
Install prerequisites for fluidsynth
apt-get update -qq && apt-get install -qq libfluidsynth1 fluid-soundfont-gm build-essential libasound2-dev libjack-dev
Install python libraries (preferrably inside a virtualenv):
pip install -r requirements.txt
Download all the data (caution, significant data sizes!):
./fetch_data.sh
Download DrumsRNN model and do data preprocessing (samples cutting, synthesis, etc.):
cd scripts
./run_all.sh
One of the sounds in 200 drums machines dataset is corrupted and should be deleted manually. You will see which in an IO error message.
Train CAEs (final version, with barkgrams)
cd scripts
python cae_training.py ../configs/cae_training_bark.yaml
Train DNN classifiers (require CAEs to be trained first):
cd scripts
python classifier_training.py ../configs/classifier_training_2.yaml
Onset detection results are in notebooks/Onsets.ipynb
Main results are in notebooks/Classification.ipynb
LM influence analysis is in notebooks/LMAnalysis.ipynb
Replication of Mehrabi's results is split between LMER.ipynb and LMER-R.ipynb, the second requires R and R kernel for Jupyter to be installed.