cwitkowitz / sparse-analytic-filters Goto Github PK

Code for the paper Learning Sparse Analytic Filters for Piano Transcription. The repository contains scripts to run and analyze a filterbank learning experiment on a piano transcription task. It is a thin layer on top of two more general repositories:

• lhvqt - filterbank learning module
• amt-tools - music transcription framework

Clone the repository and install the requirements.

git clone https://github.com/cwitkowitz/sparse-analytic-filters
pip install -r sparse-analytic-filters/requirements.txt

This will install the two aforementioned repositories along with dependencies.

The full training-testing pipeline can be run from the command line as follows:

python scripts/experiment.py

Parameters for the experiment are defined towards the top of the script. These include hyperparameters such as sampling rate or learning rate, as well as filterbank parameters.

The transcription model is defined in scripts/model.py. This script is where modifications can be made, e.g. Bernoulli dropout. It is also where KL-divergence scaling or annealing can be set.

The filterbank of a saved model checkpoint can be visualized manually as follows:

python scripts/visualize.py

The GPU to use, the model path, and the directory under which to save images are all defined at the bottom of the script.

The filterbank of a saved model checkpoint can be sonified manually as follows:

python scripts/sonify.py

The GPU to use, the model path, and the directory under which to save audio are all defined at the bottom of the script.

A saved model checkpoint can be evaluated manually as follows:

python scripts/evaluate.py

The GPU to use and the model path are defined at the top of the script.

The experiment root directory <root_dir> is one parameter defined at the top of the experiment script. Execution of scripts/experiment.py will generate the following under <root_dir>:

• n/

  Folder (beginning at n = 1) containing sacred experiment files:

  • config.json - parameter values for the experiment
  • cout.txt - contains any text printed to console
  • metrics.json - evaluation results for the experiment
  • run.json system and experiment information

  An additional folder (n += 1) with experiment files is created for each run where the name of the sacred experiment is the same.

• models/

  Folder containing saved model and optimizer states at checkpoints, as well as the events file that tensorboard reads.

• estimated/

  Folder containing frame-level and note-level predictions for all tracks in the test set. Predictions are organized within .txt files according to the MIREX I/O format for transcription.

• results/

  Folder containing individual evaluation results for each track within the test set.

• sonification/

  Folder containing sonified filters at initialization and after training completion.

• _sources/

  Folder containing copies of the script at the time(s) of execution.

• visualization/

  Folder containing visualized filters at initialization and every training checkpoint thereafter.

Additionally, ground-truth will be saved under the path specified by features_gt_cache, unless save_data=False.

During training, losses and various validation metrics can be analyzed in real-time by running:

tensorboard --logdir=<root_dir>/models --port=<port>

Here we assume the current directory within the command-line interface contains <root_dir>. <port> is an integer corresponding to an available port (port = 6006 if unspecified).

After running the command, navigate to http://localhost:port to view any reported training or validation observations within the tensorboard interface.

@inproceedings{cwitkowitz2022learning,
  title     = {Learning Sparse Analytic Filters for Piano Transcription},
  author    = {Frank Cwitkowitz and Mojtaba Heydari and Zhiyao Duan},
  year      = 2022,
  booktitle = {Proceedings of Sound and Music Computing Conference (SMC)}
}