def c_weighting_coeffs_design(sample_rate):
"""Returns b and a coeff of a C-weighting filter.
Parameters
----------
sample_rate : scalar
Sample rate of the signals that well be filtered.
Returns
-------
b, a : ndarray
Filter coefficients for a digital weighting filter.
Examples
--------
b, a = c_weighting_coeffs_design(sample_rate)
To Filter a signal use scipy lfilter:
from scipy.signal import lfilter
y = lfilter(b, a, x)
See Also
--------
a_weighting_coeffs_design : A-Weighting coefficients.
b_weighting_coeffs_design : B-Weighting coefficients.
weight_signal : Apply a weighting filter to a signal.
"""
f1 = 20.598997
f4 = 12194.217
C1000 = 0.0619
numerators = [(2 * pi * f4)**2 * (10**(C1000 / 20)), 0, 0]
denominators = convolve([1, +4 * pi * f4, (2 * pi * f4)**2],
[1, +4 * pi * f1, (2 * pi * f1)**2])
return bilinear(numerators, denominators, sample_rate)
def b_weighting_coeffs_design(sample_rate):
"""Returns `b` and `a` coeff of a B-weighting filter.
B-Weighting is no longer described in DIN61672.
Parameters
----------
sample_rate : scalar
Sample rate of the signals that well be filtered.
Returns
-------
b, a : ndarray
Filter coefficients for a digital weighting filter.
Examples
--------
>>> b, a = b_weighting_coeff_design(sample_rate)
To Filter a signal use :function: scipy.lfilter:
>>> from scipy.signal import lfilter
>>> y = lfilter(b, a, x)
See Also
--------
a_weighting_coeffs_design : A-Weighting coefficients.
c_weighting_coeffs_design : C-Weighting coefficients.
weight_signal : Apply a weighting filter to a signal.
"""
f1 = 20.598997
f2 = 158.5
f4 = 12194.217
B1000 = 0.17
numerators = [(2 * pi * f4)**2 * (10**(B1000 / 20)), 0, 0, 0]
denominators = convolve([1, +4 * pi * f4, (2 * pi * f4)**2],
[1, +4 * pi * f1, (2 * pi * f1)**2])
denominators = convolve(denominators, [1, 2 * pi * f2])
return bilinear(numerators, denominators, sample_rate)
def a_weighting_coeffs_design(sample_rate):
"""Returns b and a coeff of a A-weighting filter.
Parameters
----------
sample_rate : scalar
Sample rate of the signals that well be filtered.
Returns
-------
b, a : ndarray
Filter coefficients for a digital weighting filter.
Examples
--------
>>> b, a = a_weighting_coeff_design(sample_rate)
To Filter a signal use scipy lfilter:
>>> from scipy.signal import lfilter
>>> y = lfilter(b, a, x)
See Also
--------
b_weighting_coeffs_design : B-Weighting coefficients.
c_weighting_coeffs_design : C-Weighting coefficients.
weight_signal : Apply a weighting filter to a signal.
scipy.lfilter : Filtering signal with `b` and `a` coefficients.
"""
f1 = 20.598997
f2 = 107.65265
f3 = 737.86223
f4 = 12194.217
A1000 = 1.9997
numerators = [(2 * pi * f4)**2 * (10**(A1000 / 20.0)), 0., 0., 0., 0.]
denominators = convolve([1., +4 * pi * f4, (2 * pi * f4)**2],
[1., +4 * pi * f1, (2 * pi * f1)**2])
denominators = convolve(convolve(denominators, [1., 2 * pi * f3]),
[1., 2 * pi * f2])
return bilinear(numerators, denominators, sample_rate)
def C_weighting(self, fs):
f1 = 20.598997
f4 = 12194.217
C1000 = 0.0619
NUMs = [(2*pi*f4)**2*(10**(C1000/20.0)),0,0]
DENs = polymul([1,4*pi*f4,(2*pi*f4)**2.0],[1,4*pi*f1,(2*pi*f1)**2])
return bilinear(NUMs,DENs,fs)
def a_weighting_coeffs_design(sample_rate):
f1 = 20.598997
f2 = 107.65265
f3 = 737.86223
f4 = 12194.217
A1000 = 1.9997
numerators = [(2 * pi * f4)**2 * (10**(A1000 / 20.0)), 0., 0., 0., 0.]
denominators = convolve([1., +4 * pi * f4, (2 * pi * f4)**2],
[1., +4 * pi * f1, (2 * pi * f1)**2])
denominators = convolve(convolve(denominators, [1., 2 * pi * f3]),
[1., 2 * pi * f2])
return bilinear(numerators, denominators, sample_rate)
def A_weighting_filter(fs):
"""construct an a-weighting filter at the specified samplerate
from here: http://www.mathworks.com/matlabcentral/fileexchange/69
"""
f1, f2, f3, f4, A1000 = 20.598997, 107.65265, 737.86223, 12194.217, 1.9997
NUMs = [(2 * np.pi * f4) ** 2 * (10 ** (A1000/20)), 0, 0, 0, 0]
DENs = np.convolve([1, 4 * np.pi * f4, (2 * np.pi * f4) ** 2],
[1, 4 * np.pi * f1, (2 * np.pi * f1) ** 2], mode='full')
DENs = np.convolve(np.convolve(DENs, [1, 2 * np.pi * f3], mode='full'),
[1, 2 * np.pi * f2], mode='full')
return bilinear(NUMs, DENs, fs)
def a_weight_filter(sample_rate):
# Definition of analog A-weighting filter according to IEC/CD 1672.
f1 = 20.598997
f2 = 107.65265
f3 = 737.86223
f4 = 12194.217
A1000 = 1.9997
numerators = [(2 * pi * f4)**2 * (10**(A1000 / 20.0)), 0., 0., 0., 0.]
denominators = convolve([1., +4 * pi * f4, (2 * pi * f4)**2],
[1., +4 * pi * f1, (2 * pi * f1)**2])
denominators = convolve(convolve(denominators, [1., 2 * pi * f3]),
[1., 2 * pi * f2])
return bilinear(numerators, denominators, sample_rate)
def A_weighting_filter(fs):
"""construct an a-weighting filter at the specified samplerate
from here: http://www.mathworks.com/matlabcentral/fileexchange/69
"""
f1, f2, f3, f4, A1000 = 20.598997, 107.65265, 737.86223, 12194.217, 1.9997
NUMs = [(2 * np.pi * f4)**2 * (10**(A1000 / 20)), 0, 0, 0, 0]
DENs = np.convolve([1, 4 * np.pi * f4, (2 * np.pi * f4)**2],
[1, 4 * np.pi * f1, (2 * np.pi * f1)**2],
mode='full')
DENs = np.convolve(np.convolve(DENs, [1, 2 * np.pi * f3], mode='full'),
[1, 2 * np.pi * f2],
mode='full')
return bilinear(NUMs, DENs, fs)
def A_weighting(self, fs):
f1 = 20.598997
f2 = 107.65265
f3 = 737.86223
f4 = 12194.217
A1000 = 1.9997
NUMs = [(2*pi * f4)**2 * (10**(A1000/20)), 0, 0, 0, 0]
DENs = polymul([1, 4*pi * f4, (2*pi * f4)**2],
[1, 4*pi * f1, (2*pi * f1)**2])
DENs = polymul(polymul(DENs, [1, 2*pi * f3]),
[1, 2*pi * f2])
return bilinear(NUMs, DENs, fs)
def a_weighting_coeffs_design(sample_rate):
"""Returns b and a coeff of a A-weighting filter.
Parameters
----------
sample_rate : scalar
Sample rate of the signals that well be filtered.
Returns
-------
b, a : ndarray
Filter coefficients for a digital weighting filter.
Examples
--------
>>> b, a = a_weighting_coeff_design(sample_rate)
To Filter a signal use scipy lfilter:
>>> from scipy.signal import lfilter
>>> y = lfilter(b, a, x)
See Also
--------
b_weighting_coeffs_design : B-Weighting coefficients.
c_weighting_coeffs_design : C-Weighting coefficients.
weight_signal : Apply a weighting filter to a signal.
scipy.lfilter : Filtering signal with `b` and `a` coefficients.
"""
f1 = 20.598997
f2 = 107.65265
f3 = 737.86223
f4 = 12194.217
A1000 = 1.9997
numerators = [(2*pi*f4)**2 * (10**(A1000 / 20.0)), 0., 0., 0., 0.];
denominators = convolve(
[1., +4*pi * f4, (2*pi * f4)**2],
[1., +4*pi * f1, (2*pi * f1)**2]
)
denominators = convolve(
convolve(denominators, [1., 2*pi * f3]),
[1., 2*pi * f2]
)
return bilinear(numerators, denominators, sample_rate)
def A_weighting(Fs):
"""Design of an A-weighting filter.
B, A = A_weighting(Fs) designs a digital A-weighting filter for
sampling frequency Fs. Usage: y = lfilter(B, A, x).
Warning: Fs should normally be higher than 20 kHz. For example,
Fs = 48000 yields a class 1-compliant filter.
Originally a MATLAB script. Also included ASPEC, CDSGN, CSPEC.
Author: Christophe Couvreur, Faculte Polytechnique de Mons (Belgium)
[email protected]
Last modification: Aug. 20, 1997, 10:00am.
http://www.mathworks.com/matlabcentral/fileexchange/69
http://replaygain.hydrogenaudio.org/mfiles/adsgn.m
Translated from adsgn.m to PyLab 2009-07-14 [email protected]
References:
[1] IEC/CD 1672: Electroacoustics-Sound Level Meters, Nov. 1996.
"""
# Definition of analog A-weighting filter according to IEC/CD 1672.
f1 = 20.598997
f2 = 107.65265
f3 = 737.86223
f4 = 12194.217
A1000 = 1.9997
NUMs = [(2 * pi * f4)**2 * (10**(A1000 / 20)), 0, 0, 0, 0]
DENs = convolve([1, +4 * pi * f4, (2 * pi * f4)**2],
[1, +4 * pi * f1, (2 * pi * f1)**2],
mode='full')
DENs = convolve(convolve(DENs, [1, 2 * pi * f3], mode='full'),
[1, 2 * pi * f2],
mode='full')
# Use the bilinear transformation to get the digital filter.
# (Octave, MATLAB, and PyLab disagree about Fs vs 1/Fs)
return bilinear(NUMs, DENs, Fs)
# You probably need to specify the axis. Importing a stereo sound file with scikits.audiolab needs axis = 0, for instance:
# y = lfilter(B, A, x, axis = 0)
def b_weighting_coeffs_design(sample_rate):
"""Returns `b` and `a` coeff of a B-weighting filter.
B-Weighting is no longer described in DIN61672.
Parameters
----------
sample_rate : scalar
Sample rate of the signals that well be filtered.
Returns
-------
b, a : ndarray
Filter coefficients for a digital weighting filter.
Examples
--------
>>> b, a = b_weighting_coeff_design(sample_rate)
To Filter a signal use :function: scipy.lfilter:
>>> from scipy.signal import lfilter
>>> y = lfilter(b, a, x)
See Also
--------
a_weighting_coeffs_design : A-Weighting coefficients.
c_weighting_coeffs_design : C-Weighting coefficients.
weight_signal : Apply a weighting filter to a signal.
"""
f1 = 20.598997
f2 = 158.5
f4 = 12194.217
B1000 = 0.17
numerators = [(2*pi*f4)**2 * (10**(B1000 / 20)), 0, 0, 0];
denominators = convolve(
[1, +4*pi * f4, (2*pi * f4)**2],
[1, +4*pi * f1, (2*pi * f1)**2]
)
denominators = convolve(denominators, [1, 2*pi * f2])
return bilinear(numerators, denominators, sample_rate)
def A_weighting(Fs):
"""Design of an A-weighting filter.
B, A = A_weighting(Fs) designs a digital A-weighting filter for
sampling frequency Fs. Usage: y = lfilter(B, A, x).
Warning: Fs should normally be higher than 20 kHz. For example,
Fs = 48000 yields a class 1-compliant filter.
Originally a MATLAB script. Also included ASPEC, CDSGN, CSPEC.
Author: Christophe Couvreur, Faculte Polytechnique de Mons (Belgium)
[email protected]
Last modification: Aug. 20, 1997, 10:00am.
http://www.mathworks.com/matlabcentral/fileexchange/69
http://replaygain.hydrogenaudio.org/mfiles/adsgn.m
Translated from adsgn.m to PyLab 2009-07-14 [email protected]
References:
[1] IEC/CD 1672: Electroacoustics-Sound Level Meters, Nov. 1996.
"""
# Definition of analog A-weighting filter according to IEC/CD 1672.
f1 = 20.598997
f2 = 107.65265
f3 = 737.86223
f4 = 12194.217
A1000 = 1.9997
NUMs = [(2 * pi * f4)**2 * (10**(A1000/20)), 0, 0, 0, 0]
DENs = convolve([1, +4 * pi * f4, (2 * pi * f4)**2],
[1, +4 * pi * f1, (2 * pi * f1)**2], mode='full')
DENs = convolve(convolve(DENs, [1, 2 * pi * f3], mode='full'),
[1, 2 * pi * f2], mode='full')
# Use the bilinear transformation to get the digital filter.
# (Octave, MATLAB, and PyLab disagree about Fs vs 1/Fs)
return bilinear(NUMs, DENs, Fs)
# You probably need to specify the axis. Importing a stereo sound file with scikits.audiolab needs axis = 0, for instance:
# y = lfilter(B, A, x, axis = 0)
def c_weighting_coeffs_design(sample_rate):
"""Returns b and a coeff of a C-weighting filter.
Parameters
----------
sample_rate : scalar
Sample rate of the signals that well be filtered.
Returns
-------
b, a : ndarray
Filter coefficients for a digital weighting filter.
Examples
--------
b, a = c_weighting_coeffs_design(sample_rate)
To Filter a signal use scipy lfilter:
from scipy.signal import lfilter
y = lfilter(b, a, x)
See Also
--------
a_weighting_coeffs_design : A-Weighting coefficients.
b_weighting_coeffs_design : B-Weighting coefficients.
weight_signal : Apply a weighting filter to a signal.
"""
f1 = 20.598997
f4 = 12194.217
C1000 = 0.0619
numerators = [(2*pi * f4)**2 * (10**(C1000 / 20)), 0, 0]
denominators = convolve(
[1, +4*pi * f4, (2*pi * f4)**2],
[1, +4*pi * f1, (2*pi * f1)**2]
)
return bilinear(numerators, denominators, sample_rate)
