Lowpass filter with a time-varying cutoff frequency, with Python

Question

How to apply a lowpass filter, with cutoff frequency varying linearly (or with a more general curve than linear) from eg 10000hz to 200hz along time , with numpy/scipy and possibly no other library?

Example:

• at 00:00,000, lowpass cutoff = 10000hz
• at 00:05,000, lowpass cutoff = 5000hz
• at 00:09,000, lowpass cutoff = 1000hz
• then cutoff stays at 1000hz during 10 seconds, then cutoff decreases down to 200hz

Here is how to do a simple 100hz lowpass:

from scipy.io import wavfile
import numpy as np
from scipy.signal import butter, lfilter

sr, x = wavfile.read('test.wav')
b, a = butter(2, 100.0 / sr, btype='low')  # Butterworth
y = lfilter(b, a, x)
wavfile.write('out.wav', sr, np.asarray(y, dtype=np.int16))

but how to make the cutoff vary?

Note: I've already read Applying time-variant filter in Python but the answer is quite complex (and it applies to many kinds of filter in general).

Answer 1

One comparatively easy method is to keep the filter fixed and modulate signal time instead. For example, if signal time runs 10x faster a 10KHz lowpass will act like a 1KHz lowpass in standard time.

To do this we need to solve a simple ODE

dy       1
--  =  ----
dt     f(y)

Here t is modulated time y real time and f the desired cutoff at time y .

Prototype implementation:

from __future__ import division
import numpy as np
from scipy import integrate, interpolate
from scipy.signal import butter, lfilter, spectrogram

slack_l, slack = 0.1, 1
cutoff = 50
L = 25

from scipy.io import wavfile
sr, x = wavfile.read('capriccio.wav')
x = x[:(L + slack) * sr, 0]
x = x

# sr = 44100
# x = np.random.normal(size=((L + slack) * sr,))

b, a = butter(2, 2 * cutoff / sr, btype='low')  # Butterworth

# cutoff function
def f(t):
    return (10000 - 1000 * np.clip(t, 0, 9) - 1000 * np.clip(t-19, 0, 0.8)) \
        / cutoff

# and its reciprocal
def fr(_, t):
    return cutoff / (10000 - 1000 * t.clip(0, 9) - 1000 * (t-19).clip(0, 0.8))

# modulate time
# calculate upper end of td first
tdmax = integrate.quad(f, 0, L + slack_l, points=[9, 19, 19.8])[0]
span = (0, tdmax)
t = np.arange(x.size) / sr
tdinfo = integrate.solve_ivp(fr, span, np.zeros((1,)),
                             t_eval=np.arange(0, span[-1], 1 / sr),
                             vectorized=True)
td = tdinfo.y.ravel()
# modulate signal
xd = interpolate.interp1d(t, x)(td)
# and linearly filter
yd = lfilter(b, a, xd)
# modulate signal back to linear time
y = interpolate.interp1d(td, yd)(t[:-sr*slack])

# check
import pylab
xa, ya, z = spectrogram(y, sr)
pylab.pcolor(ya, xa, z, vmax=2**8, cmap='nipy_spectral')
pylab.savefig('tst.png')

wavfile.write('capriccio_vandalized.wav', sr, y.astype(np.int16))

Sample output:

Spectrogram of first 25 seconds of BWV 826 Capriccio filtered with a time varying lowpass implemented via time bending.

Answer 2

you can use scipy.fftpack.fftfreq and scipy.fftpack.rfft to set thresholds

fft = scipy.fftpack.fft(sound)
freqs = scipy.fftpack.fftfreq(sound.size, time_step)

for the time_step I did twice the sampling rate of the sound

fft[(freqs < 200)] = 0

this would set all set all frequencies less than 200 hz to zero

for the time varying cut off, I'd split the sound and apply the filters then. assuming the sound has a sampling rate of 44100, the 5000hz filter would start at sample 220500 (five seconds in)

10ksound = sound[:220500]
10kfreq = scipy.fftpack.fftreq(10ksound.size, time_step)
10kfft = scipy.fftpack.fft(10ksound)
10kfft[(10kfreqs < 10000)] = 0

then for the next filter:

5ksound = sound[220500:396900]
5kfreq = scipy.fftpack.fftreq(10ksound.size, time_step)
5kfft = scipy.fftpack.fft(10ksound)
5kfft[(5kfreqs < 5000)] = 0

etc

edit: to make it "sliding" or a gradual filter instead of piece wise, you could make the "pieces" much smaller and apply increasingly bigger frequency thresholds to the corresponding piece(5000 -> 5001 -> 5002)