alignednotemodel v1.1.0

alignednotemodels

Python tools to compute note models from note-level audio-score alignment.

Currently the algorithm computes the stable pitch and a pitchdistribution of each aligned note.

Usage

from alignednotemodel.alignednotemodel import AlignedNoteModel
alignedNoteModel = AlignedNoteModel(kernel_width=7.5, step_size=7.5, pitch_threshold=50)

note_models, pitch_distribution, new_tonic = alignedNoteModel.get_models(pitch, aligned_notes,
    tonic_symbol)

Instantiation parameters are:

# kernel_width    : The width of the Gaussian kernel used to compute the pitch distribution 
#                   (default: 7.5 cent ~ 1/3 Hc)
# step_size       : The step size between each bin of the pitch distribution (default: 7.5 cent 
#                   ~ 1/3 Hc)
# pitch_threshold : Max cent difference for two pitch calues to be considered close. Used in
#                   stable pitch computation (default: 50 cent, a quarter tone)

The inputs for the get_models method are:

# pitch           : an n-by-2 matrix, where the values in the first column are 
#                   the timestamps and the values in the second column are frequency 
#                   values
# aligned_notes   : the list of aligned notes. This is read from the alignedNotes.json 
#                   output from the fragmentLinker (https://github.com/sertansenturk/fragmentLinker) 
#                   repository 
# tonic_symbol    : The tonic symbol in the symbTr format (e.g. B4b1)

The outputs are:

# note_models        : The model for each note symbol
# pitch_distribution : The pitch distribution computed from the pitch input
# new_tonic          : The updated tonic according to the note model of the tonic symbol

Please refer to note_model.ipynb for an interactive demo.

Installation

If you want to install the repository, it is recommended to install the package and dependencies into a virtualenv. In the terminal, do the following:

virtualenv env
source env/bin/activate
python setup.py install

If you want to be able to edit files and have the changes be reflected, then
install the repository like this instead

pip install -e .

The algorithm uses several modules in Essentia. Follow the instructions to install the library.

Now you can install the rest of the dependencies:

pip install -r requirements

Changelog

• Refactoring to improve code quality, readability and functionality

Authors

Sertan Şentürk
contact@sertansenturk.com

Reference

Thesis