In [8]:
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt
import matplotlib.ticker
import FilterShapes as filters


For today's article, we'll be talking about filters. So far in this series I haven't spoken too much about filters, which might seem odd considering how much of signal processing in general is all about filters. The reason I've avoided filters is that most filters in audio signal processing are implemented as linear processors, and I've been focusing on nonlinear processing concepts.

However, this does not have to be the case. For instance, Vadim Zavalishin at Native Instruments has written extensively about including nonlinear elements in ladder filters and state variable filters, in ways that accurately reflect how these filters often behave in the analog world. Actually, Vadim's entire book, The Art of Virtual Analog Filter Design, is a fantastic resource, that I highly recommend for anyone with an interest in filter design for audio. That said, Vadim focuses mostly on the virtual analog world, and in this series of articles I've been striving to construct signal processing ideas that can be conceived and implemented entirely in the digital realm.

That said, one of the coolest things about analog filters is the way that they resonate. A "resonance" occurs in a filter when the filter is tuned to amplify a certain frequency, much like a guitar string is designed to resonate at the frequency to which it is tuned. In the analog world, whenever something is amplified, we run the risk of moving into nonlinear territory, where saturation and distortion can occur. One of the reasons that analog filters often sound "warm" to musicians and engineers is due to the fact that as the filters resonate, they begin to saturate as well. Thinking about this got me thinking: could there be a way to alter some existing digital filter forms so that any resonance in the filter automatically undergoes some saturation? This idea is what I'll be developing below...

To begin, I'd like to introduce the idea of a "biquad" filter, which is a short way for DSP engineers to refer to a 2nd-order filter. "2nd-order" means that the filter has "memory" that extends back to the two previous timesteps that it has computed. In other words, the current output of the filter depends not just on the current input, but also on the previous two inputs and outputs. DSP engineers like biquad filters a lot, because any filter can be broken down into a "cascade" of biquad filters.

### Transposed Direct Form-II¶

There are several way to implement a biquad filter, but one of my favorites is the so-called "Transposed Direct Form-II". This a rather complicated name for a relatively simple thing, but more information on why this name exists, as well as other filter forms, can be found in Julius Smith's wonderful Introduction to Digital Filters.

In [9]:
import SchemDraw
from SchemDraw import dsp
import SchemDraw.elements as e

d = SchemDraw.Drawing(fontsize=12)
L1 = d.add (dsp.LINE, d='right', l=1, color='white')
d.add (dsp.AMP, toplabel='$b_0$', color='white')

d.add (dsp.LINE, xy=L1.end, d='down', l=3, color='white')
L2 = d.add (dsp.LINE, d='right', l=1, color='white')
d.add (dsp.AMP, toplabel='$b_1$', color='white')
d.add (dsp.LINE, d='up', xy=SUM1.N, l=0.375, color='white')
d.add (dsp.BOX, label='$z^{-1}$', color='white')

d.add (dsp.LINE, xy=L2.start, d='down', l=3, color='white')
d.add (dsp.AMP, toplabel='$b_2$', color='white')
d.add (dsp.LINE, d='up', xy=SUM2.N, l=0.375, color='white')
d.add (dsp.BOX, label='$z^{-1}$', color='white')

Y = d.add (dsp.LINE, d='right', xy=SUM0.E, l=2.5, color='white')
L3 = d.add (dsp.LINE, d='left', l=1, color='white')
A1 = d.add (dsp.AMP, toplabel='$-a_1$', color='white')

d.add (dsp.LINE, xy=L3.start, d='down', l=3, color='white')
A1 = d.add (dsp.AMP, toplabel='$-a_2$', color='white')

d.add (dsp.LINE, xy=Y.end, d='right', l=1, color='white')

d.draw()


Above we see a signal flow diagram for Transposed Direct Form-II (TDF-II). TDF-II is a favorite of mine for several reasons. For one thing, it is pretty efficient in that it only uses a handful of multiplies and two delay elements. It also has some nice numerical properties for implementing highly resonant filters. Another convenient thing about TDF-II which will be useful here, is that we can easily see what values are stored in the unit delay ($z^{-1}$) elements. We'll refer to these values as the "state" of the filter.

### Filter Shapes¶

There are several particular filter "shapes" that are often useful for processing audio, and all of them can be constructed using a TDF-II structure. In this article, I'm going to focus on 6 shapes: Highpass, Lowpass, High Shelf, Low Shelf, Bell, and Notch. Plots of the frequency response for each of these filters can be found below. For instructions on how to implement these filters using a biquad filter, please check out Robert Bristow-Johnson's Audio EQ Cookbook, another indespensible resource for audio DSP engineers.

In [10]:
def plotFilter (b, a, title, ylim, fs=44100):
w, H = signal.freqz (b, a, fs=fs)
plt.figure()
plt.semilogx (w, 20 * np.log10 (np.abs (H)))
plt.title (title)
plt.ylim (ylim)
plt.gca().xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.xlabel ('Frequency [Hz]')
plt.ylabel ('Magnitude [dB]')
plt.xlim (20, 20000)

fs = 44100
b, a = filters.calcCoefsLPF2 (1000, 0.707*2, fs)
plotFilter (b, a, 'Lowpass Filter', ylim=(-30, 10))

b, a = filters.calcCoefsHPF2 (1000, 0.707*2, fs)
plotFilter (b, a, 'Highpass Filter', ylim=(-30, 10))

b, a = filters.calcCoefsLowShelf (1000, 0.707*2, 2, fs)
plotFilter (b, a, 'Low Shelf Filter', ylim=(-5, 10))

b, a = filters.calcCoefsHighShelf (1000, 0.707*2, 2, fs)
plotFilter (b, a, 'High Shelf Filter', ylim=(-5, 10))

b, a = filters.calcCoefsBell (1000, 0.707*2, 2, fs)
plotFilter (b, a, 'Bell Filter', ylim=(-5, 10))

b, a = filters.calcCoefsNotch (1000, 0.707*2, fs)
plotFilter (b, a, 'Notch Filter', ylim=(-30, 10))

d:\Documents\CCRMA\Research\Complex_Nonlinearities\NonlinearBiquad\nlBiquadWriteup.py:4: RuntimeWarning: divide by zero encountered in log10
import matplotlib.pyplot as plt


## Nonlinear Resonance¶

In order to make any TDF-II filter exhibit nonlinear resonance, we can simply saturate the states. In the following examples, I will demonstrate three saturating nonlinearities: a soft clipper, a hard clipper, and a $\tanh$ clipper. The mathematics that describe why this works and ensure that we don't accidentally make an unstable filter are a litle bit complex, and I'm not going to explain them here, partly because I'm still wrapping my head around them myself. The resulting signal flow diagram will look something like this (the "NL" blocks represent nonlinear elements):

In [11]:
d = SchemDraw.Drawing(fontsize=12)
L1 = d.add (dsp.LINE, d='right', l=1, color='white')
d.add (dsp.AMP, toplabel='$b_0$', color='white')

d.add (dsp.LINE, xy=L1.end, d='down', l=4.25, color='white')
L2 = d.add (dsp.LINE, d='right', l=1, color='white')
d.add (dsp.AMP, toplabel='$b_1$', color='white')
d.add (dsp.LINE, d='up', xy=SUM1.N, l=0.25, color='white')
d.add (dsp.BOX, label='$z^{-1}$', color='white')

d.add (dsp.LINE, xy=L2.start, d='down', l=4.25, color='white')
d.add (dsp.AMP, toplabel='$b_2$', color='white')
d.add (dsp.LINE, d='up', xy=SUM2.N, l=0.25, color='white')
d.add (dsp.BOX, label='$z^{-1}$', color='white')

Y = d.add (dsp.LINE, d='right', xy=SUM0.E, l=2.5, color='white')
L3 = d.add (dsp.LINE, d='left', l=1, color='white')
A1 = d.add (dsp.AMP, toplabel='$-a_1$', color='white')

d.add (dsp.LINE, xy=L3.start, d='down', l=4.25, color='white')
A1 = d.add (dsp.AMP, toplabel='$-a_2$', color='white')

d.add (dsp.LINE, xy=Y.end, d='right', l=1, color='white')

d.draw()


### Frequency Responses¶

To get a sense of what this modified structure does to our filter, it would be nice to look at the frequency response of the filter. Unfortunately, as I mentioned in my introductory article for this series, a nonlinear system does not have a frequency response the same way that a linear system does. However, what we can do is choose some level (often called an "operating point"), and see how our system reacts to various frequencies at that level. Below, we show the "frequency response" for a low shelf filter and a bell filter, each with $\tanh$ nonlinearities, at various operating points.

In [12]:
def chirpLog (f0, f1, duration, fs):
N = int (duration * fs)
n = np.arange (N)

beta = N / np.log(f1 / f0)
phase = 2 * np.pi * beta * f0 * (pow(f1 / f0, n / N) - 1.0)
phi = np.pi / 180

return np.cos ((phase + phi)/fs)

def chrp2ir (ss, rs):
N = max (len (rs), len (ss))
SS = np.fft.fft (ss, n=N)
RS = np.fft.fft (rs, n=N)

H = RS/SS
h = np.real (np.fft.ifft (H))
return h

def __init__ (self):
self.z = np.zeros (3)
self.b = np.array ([1, 0, 0])
self.a = np.array ([1, 0, 0])
self.saturator = lambda x : x

def setCoefs (self, b, a):
assert (np.size (b) == np.size (self.b))
assert (np.size (a) == np.size (self.a))
self.b = np.copy (b)
self.a = np.copy (a)

# Direct-Form II, transposed
def processSample (self, x):
y = self.z[1] + self.b[0]*x
self.z[1] = self.saturator (self.z[2] + self.b[1]*x - self.a[1]*y)
self.z[2] = self.saturator (self.b[2]*x - self.a[2]*y)
return y

def processBlock (self, block):
for n in range (len (block)):
block[n] = self.processSample (block[n])
return block

In [13]:
def plotFilterResponse (biquad, fs, gain=0.1):
x = gain*chirpLog (20, 20000, 1, fs)
N = len (x)

h = chrp2ir (x, y)

f = np.linspace (0, fs/2, num=N/2+1)
H = np.fft.rfft (h)
plt.semilogx (f, 20 * np.log10 (np.abs (H)))

def plotNonlinearFilterResponse (b, a, title, gains=[0.5, 0.25, 0.1]):
plt.figure()
legend = []
for gain in gains:
nlBQ.setCoefs (b, a)
nlBQ.saturator = lambda x : np.tanh (x)
plotFilterResponse (nlBQ, fs, gain=gain)
legend.append ('Nonlinear (gain={})'.format (gain))

normalBQ.setCoefs (b, a)
plotFilterResponse (normalBQ, fs)
legend.append ('Linear')

plt.xlim (20, 20000)
plt.gca().xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())

plt.title (title)
plt.xlabel ('Frequency [Hz]')
plt.ylabel ('Magnitude [dB]')
plt.legend (legend)

fs = 44100
b, a = filters.calcCoefsLowShelf (1000, 0.707*2, 2, fs)
plotNonlinearFilterResponse (b, a, 'Nonlinear Low Shelf')

b, a = filters.calcCoefsBell (1000, 0.707, 4, fs)
plotNonlinearFilterResponse (b, a, 'Nonlinear Bell Filter', gains=[1.5, 0.4, 0.1])
plt.ylim (-2, 15)

d:\Documents\CCRMA\Research\Complex_Nonlinearities\NonlinearBiquad\nlBiquadWriteup.py:8: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.
#%% [markdown]

Out[13]:
(-2, 15)