A Python implementation of the Recurrence Period Density Entropy (RPDE)
A full-python implementation of the Recurrence Period Density Entropy metric. It's based on the algorithm described in ^1, and on Max Little's R (and C) implementation in ^2. It relies on Numba to make the return distance computation as fast as possible without having to resort to Cython or C/C++ bindings.
This package needs python >= 3.6, and relies on Numba, Scipy, and Numpy. It's available on pypi, so a simple:
pip install pyrpde
should do the trick.
There pretty much is only one function in this package that you should use,
Here are its arguments:
Arguments: ---------- time_series: np.ndarray The input time series. Has to be float32, normalized to [-1,1] dim: int The dimension of the time series embeddings. Defaults to 4 tau: int The "stride" between each of the embedding points in a time series' embedding vector. Should be adjusted depending on the sampling rate of your input data. Defaults to 35. epsilon: float The size of the unit ball described in the RPDE algorithm. Defaults to 0.12. tmax: int, optional Maximum return distance (n1-n0), return distances higher than this are ignored. If set, can greatly improve the speed of the distance histogram computation (especially if your input time series has a lot of points). Defaults to None. parallel: boolean, optional Use the parallelized Numba implementation. The parallelization overhead might make this slower in certain situations. Defaults to True. Returns ------- rpde: float Value of the RPDE histogram: np.ndarray 1-dimensional array corresponding to the histogram of the return distances
Here's an example:
from pyrpde import rpde from scipy.io.wavfile import read # make sure your audio data is in float32. Else, either use librosa or # normalize it to [-1,1] by dividing it by 2 ** 16 if it's 16bit PCM rate, data = read("audio_data.wav") entropy, histogram = rpde(data, tau=30, dim=4, epsilon=0.01, tmax=1500)
Citing this package
This package was implemented as part of the experimental protocol used in Riad et Al. You can find this implementation of the RPDE being used in the paper's repository. If you're willing to make use of this package for your own research, you're welcome to cite our paper:
Riad, R, Titeux, H, Lemoine, L., Montillot J. Hamet Bagnou, J. Cao, X., Dupoux, E & Bachoud-Lévi A.-C. Vocal markers from sustained phonation in Huntington's Disease. In: INTERSPEECH-2020
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