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Published June 2, 2016 | Version v1.1.1
Software Open

alignednotemodel: alignednotemodel v1.1.1

Authors/Creators

  • 1. Universitat Pompeu Fabra

Description

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
  • Updated PitchDistribution requirements (morty)
Authors

Sertan Şentürk contact@sertansenturk.com

Reference

Thesis

Files

alignednotemodel-v1.1.1.zip

Files (451.6 kB)

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md5:e15d153007577acb7d76da3ef92f13a1
451.6 kB Preview Download

Additional details

Related works

Is supplement to
https://github.com/sertansenturk/alignednotemodel/tree/v1.1.1 (URL)