"""
| Description: libf0 yin implementation
@@ -170,17 +82,19 @@ Source code for libf0.pyin
| License: The MIT license, https://opensource.org/licenses/MIT
| This file is part of libf0.
"""
-import numpy as np
-from scipy.special import beta, comb # Scipy library for binomial beta distribution
-from scipy.stats import triang # Scipy library for triangular distribution
-from .yin import cumulative_mean_normalized_difference_function, parabolic_interpolation
-from numba import njit
+import numpy as np
+from scipy.special import beta, comb # Scipy library for binomial beta distribution
+from scipy.stats import triang # Scipy library for triangular distribution
+from .yin import cumulative_mean_normalized_difference_function, parabolic_interpolation
+from numba import njit
# pYIN estimate computation
-[docs]def pyin(x, Fs=22050, N=2048, H=256, F_min=55.0, F_max=1760.0, R=10, thresholds=np.arange(0.01, 1, 0.01),
+
+
[docs]
+
def pyin(x, Fs=22050, N=2048, H=256, F_min=55.0, F_max=1760.0, R=10, thresholds=np.arange(0.01, 1, 0.01),
beta_params=[1, 18], absolute_min_prob=0.01, voicing_prob=0.5):
-
"""
+
"""
Implementation of the pYIN F0-estimation algorithm.
.. [#] Matthias Mauch and Simon Dixon.
@@ -220,6 +134,13 @@
Source code for libf0.pyin
conf : ndarray
Confidence
"""
+
+ if F_min > F_max:
+ raise Exception("F_min must be smaller than F_max!")
+
+ if F_min < Fs/N:
+ raise Exception(f"The condition (F_min >= Fs/N) was not met. With Fs = {Fs}, N = {N} and F_min = {F_min} you have the following options: \n1) Set F_min >= {np.ceil(Fs/N)} Hz. \n2) Set N >= {np.ceil(Fs/F_min).astype(int)}. \n3) Set Fs <= {np.floor(F_min * N)} Hz.")
+
x_pad = np.concatenate((np.zeros(N // 2), x, np.zeros(N // 2))) # Add zeros for centered estimates
# Compute Beta distribution
@@ -267,9 +188,12 @@ Source code for libf0.pyin
-[docs]@njit
-
def refine_estimates_yin(f0, p_orig, val_orig, Fs, tol):
-
"""
+
+
+
[docs]
+
@njit
+
def refine_estimates_yin(f0, p_orig, val_orig, Fs, tol):
+
"""
Refine estimates using YIN CMNDF information.
Parameters
@@ -308,10 +232,13 @@
Source code for libf0.pyin
-
[docs]@njit
-
def probabilistic_thresholding(cmndf, thresholds, p_min, p_max, absolute_min_prob, F_axis, Fs, beta_distr,
+
+
+
[docs]
+
@njit
+
def probabilistic_thresholding(cmndf, thresholds, p_min, p_max, absolute_min_prob, F_axis, Fs, beta_distr,
parabolic_interp=True):
-
"""
+
"""
Probabilistic thresholding of the YIN CMNDF.
Parameters
@@ -413,10 +340,13 @@
Source code for libf0.pyin
return O_m, lag_thr, val_thr
-
[docs]@njit
-
def yin_multi_thr(x, Fs, N, H, F_min, F_max, thresholds, beta_distr, absolute_min_prob, F_axis, voicing_prob,
+
+
+
[docs]
+
@njit
+
def yin_multi_thr(x, Fs, N, H, F_min, F_max, thresholds, beta_distr, absolute_min_prob, F_axis, voicing_prob,
parabolic_interp=True):
-
"""
+
"""
Applies YIN multiple times on input audio signals using different thresholds for CMNDF.
Parameters
@@ -465,7 +395,7 @@
Source code for libf0.pyin
p_max = int(np.ceil(Fs / F_min)) # period of minimal frequency in frames
if p_max > N:
- raise Exception("N needs to be <= Fs/F_min!")
+ raise Exception("The condition (Fmin >= Fs/N) was not met.")
rms = np.zeros(M) # RMS Power
O = np.zeros((2 * B, M)) # every voiced state has an unvoiced state (important for later HMM modeling)
@@ -497,9 +427,12 @@ Source code for libf0.pyin
return O, rms, p_orig, val_orig[docs]@njit
-
def compute_transition_matrix(M, triang_distr):
-
"""
+
+
+
[docs]
+
@njit
+
def compute_transition_matrix(M, triang_distr):
+
"""
Compute a transition matrix for PYIN Viterbi.
Parameters
@@ -539,9 +472,12 @@
Source code for libf0.pyin
-
[docs]@njit
-
def viterbi_pyin(A, C, O):
-
"""Viterbi algorithm (pYIN variant)
+
+
+
[docs]
+
@njit
+
def viterbi_pyin(A, C, O):
+
"""Viterbi algorithm (pYIN variant)
Args:
A : ndarray
@@ -584,9 +520,12 @@
Source code for libf0.pyin
return idxs.astype(np.int32)
-
[docs]@njit
-
def viterbi_log_likelihood(A, C, B_O):
-
"""Viterbi algorithm (log variant) for solving the uncovering problem
+
+
+
[docs]
+
@njit
+
def viterbi_log_likelihood(A, C, B_O):
+
"""Viterbi algorithm (log variant) for solving the uncovering problem
Notebook: C5/C5S3_Viterbi.ipynb
@@ -630,60 +569,45 @@
Source code for libf0.pyin
-
[docs]@njit
-
def delete_numba(arr, num):
-
"""Delete number from array, Numba compatible. Inspired by:
+
+
+
[docs]
+
@njit
+
def delete_numba(arr, num):
+
"""Delete number from array, Numba compatible. Inspired by:
https://stackoverflow.com/questions/53602663/delete-a-row-in-numpy-array-in-numba
"""
mask = np.zeros(len(arr), dtype=np.int64) == 0
mask[np.where(arr == num)[0]] = False
return arr[mask]
+
-
+ Built with
Sphinx using a
+
theme
+ provided by
Read the Docs.
+
+
-
-
-
-
-
-
-
-
-
-
+
\ No newline at end of file
diff --git a/docs/build/html/_modules/libf0/salience.html b/docs/build/html/_modules/libf0/salience.html
index bd74ac0..404e9e6 100644
--- a/docs/build/html/_modules/libf0/salience.html
+++ b/docs/build/html/_modules/libf0/salience.html
@@ -1,98 +1,47 @@
-
+
-
-
-
-
-
libf0.salience — libf0 1.0.0 documentation
-
-
-
-
-
-
-
-
-
-
-
-
+
+
+
libf0.salience — libf0 1.0.3 documentation
+
+
-
-
-
-
-
-
-
-
-
-
-
-
+
+
+
+
-
-
-
+
-
-
-
-
-