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pypYIN

python pYIN

A python version of pYIN of Matthias Mauch
Pitch and note tracking in monophonic audio

pYIN project page

https://code.soundsoftware.ac.uk/projects/pyin

Dependencies

Numpy
Scipy
Essentia

Usage

Initialise:

Here are the parameters which need to be initialised before executing the main program:

inputSampleRate: sampling rate stepSize: hopSize
blockSize: frameSize
lowAmp(0,1): RMS of audio frame under lowAmp will be considered non voiced
onsetSensitivity: high value means note is easily be separated into two notes if low amplitude is presented.
pruneThresh(second): discards notes shorter than this threshold

Output:

Transcribed notes in Hz
Smoothed pitch track
Pitch tracks of transcribed notes in MIDI note number

Other issues:

See demo.py

License

Copyright (C) 2015 Music Technology Group - Universitat Pompeu Fabra

This file is part of pypYIN

pypYIN is free software: you can redistribute it and/or modify it under
the terms of the GNU Affero General Public License as published by the Free
Software Foundation (FSF), either version 3 of the License, or (at your
option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details.

You should have received a copy of the Affero GNU General Public License
version 3 along with this program. If not, see http://www.gnu.org/licenses/

If you have any problem about this python version code, please contact: Rong Gong
[email protected]

If you have any problem about this algorithm, I suggest you to contact: Matthias Mauch
[email protected] who is the original C++ version author of this algorithm

If you want to refer this code, please consider these articles:

M. Mauch and S. Dixon,
“pYIN: A Fundamental Frequency Estimator Using Probabilistic Threshold Distributions”,
in Proceedings of the IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2014), 2014.

M. Mauch, C. Cannam, R. Bittner, G. Fazekas, J. Salamon, J. Dai, J. Bello and S. Dixon,
“Computer-aided Melody Note Transcription Using the Tony Software: Accuracy and Efficiency”,
in Proceedings of the First International Conference on Technologies for
Music Notation and Representation, 2015.

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