lhvqt 0.5.3

Frontend filterbank learning module with HVQT initialization capabilities

Learnable Harmonic Variable-Q Transform (LHVQT)

Implements a frontend filterbank learning module which can be initialized with complex weights for a Variable-Q Transform (lvqt_orig.py). Several techniques and variations of the module are also implemented, including:

• Multi-channel (harmonic) structure (lhvqt.py)
• Real-only weights (lvqt_real.py)
• Hilbert Transform for analytic filters (lvqt_hilb.py)
• Harmonic comb initialization (lhvqt_comb.py)
• Variational dropout for 1D convolutional layer (variational.py)

The repository was created for my Master's Thesis, End-to-End Music Transcription Using Fine-Tuned Variable-Q Filterbanks. It has since been updated with various improvements, and to support my new work, Learning Sparse Analytic Filters for Piano Transcription.

Installation

Standard (PyPI)

Recommended for standard/quick usage

pip install lhvqt

Cloning Repository

Recommended for running examples or making experimental changes.

git clone https://github.com/cwitkowitz/lhvqt
pip install -e lhvqt

Usage

Several examples of instantiation, inference, and visualization are provided under the examples sub-directory. A full-blown training, visualization, and evaluation example for piano transcription can be found at https://github.com/cwitkowitz/sparse-analytic-filters.

Cite

Please cite whichever is more relevant to your usage.

SMC 2022 Paper

@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)}
}

Master's Thesis

@mastersthesis{cwitkowitz2019end,
  title   = {End-to-End Music Transcription Using Fine-Tuned Variable-{Q} Filterbanks},
  author  = {Cwitkowitz, Frank},
  year    = 2019,
  school  = {Rochester Institute of Technology}
}