Available via license: CC BY 4.0
Content may be subject to copyright.
Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Pr ocessing
Volume 2011, Article ID 629309, 11 pages
doi:10.1155/2011/629309
Research Ar ticle
Real-Time Guitar Preamp Simulation
Using Modified Blockwise Method and Approximat ions
Jaromir Macak and Jir i Schimmel
Faculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech Republic
Correspondence should be addressed to Jaromir Macak, jaromir.macak@phd.feec.vutbr.cz
Received 14 September 2010; Revised 12 December 2010; Accepted 27 January 2011
Academic Editor: Vesa Valimaki
Copyright © 2011 J. Macak and J. Schimmel. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
The designing of algorithms for real-time digital simulation of analog effects and a mplifiers brings two contradictory requirements:
accuracy versus computational efficiency. In this paper, the simulation of a t ypical guitar tube preamp using an approximation of
the solution of differential equations is discussed w ith regard to accuracy and computational complexity. The s olution of circuit
equations is precomputed and stored in N-D tables. The stored values are approximated, and therefore different approximation
techniques are investigated as well. The approximated functions are used for output sig nal computation and also for circuit state
update. The designed algorithm is compared to the numerical solution of the given preamp and also to the real preamp.
1. Introduction
The real-time digital simulation of analog guitar effects and
amplifiers has always been, unfortunately, a compromise
between accuracy and speed of a simulation algorithm.
There are many different approaches to the simulation,
such as a black box approach, a white box (informed)
approach, circuit-based approaches, and so forth [1]. All
these algorithms differ in accuracy of the simulation as well as
in computational complexity. The circuit-based techniques
usually offer the best accuracy because a simulated circuit
is exactly described by circuit equations, which are often
differential equations. Nevertheless, the computational com-
plexity needed for a solution of these equations is very high
[2]. Hence, more efficient algorithms for the solution of
the equations must be found. This can often be made by
neglecting unimportant factors, for example, tube heating,
decomposing into separate blocks [3], and decomposing
into a linear and nonlinear part. Subsequently, the separate
simplified block can be described by a new set of circuit
equations that can be solved using several methods, for
example, digital filters and waveshaping [3], Hammerstein
model [4]. The triode amplifier simulation using wave digital
filters is described in [5, 6] and an enhanced tube model
with a grid current is presented in [ 7]. A decomposition
into a linear dynamic and a nonlinear static part is used in
nonlinear state-space formulations [8, 9] and this method
was automated in [10], where the algorithm parameters
are derived from the SPICE netlist. An extended state-
space representation was used in [11]forguitarpoweramp
simulation. A direct solution of nonlinear circuit ordinary
differential equations (ODEs) was used in [2, 12], but
the computational requirements are high. Therefore, an
approximation of the solution of ODEs was used in [13].
However, an approximation of nonlinear functions are also
used in [6, 10].
In this paper, a typical guitar preamp is simulated using
the approximation of a solution of differential equations.
First of all, the guitar preamp is decomposed into separate
blocks. It is necessary to find a proper division, because it can
influence the accuracy of the whole simulation. A commonly
used division into blocks does not consider mutual interac-
tions between connected blocks. As was shown in [13, 14],
the simulation fails especially if the load of the simulated
block is nonlinear, which is t ypical for circuits with tubes.
Therefore, a modification designed in [13] and investigated
in [14] must be used in order to get good simulation results if
the blocks are highly nonlinear and connected in series. After
2 EURASIP Journal on Advances in Signal Processing
the decomposition, the new set of differential equations is
approximated according to [13]. The chosen approximation
markedly affects the accuracy of the algorithm as well as
the computational complexity. Therefore, several t ypes of
approximations will be discussed.
Considering real-time simulation, the computational
complexity must be inv estigated. Commonly used electronic
circuit simulators has a computational complexity given
by O(N
1.4
)whereN is the number of circuit nodes [1].
However, this notation does not determine the real compu-
tational complexity, that is, the real number of mathematical
operations of the algorithm. Thus in this paper, the whole
guitar preamp is numerically simulated using a similar
approach as in the electronic circuit simulators and the
number of instructions required for one signal sample is
computed. It is compared to the number of instr u ctions of
the numerical simulation based on the division into blocks
and also to the number of instructions of the approximated
solution of the given circuit.
Finally, t he accuracy of the numerical simulation of the
whole preamp must be compared to the simulation using
approximation and also to a real guitar preamp.
2. Guitar Preamp Simulation
A guitar tube preamp usually consists of several triode
amplifiers connected in series. The separate triode amplifiers
are decoupled using decoupling capacitors in order to
remove DC. The number of triode amplifiers in the circuit
depends on a given topology and therefore it can differ for
different preamps. Guitar preamps usually contain two to five
triode amplifiers. They are usually connected as a common-
cathode amplifier. However, the preamp can also contain a
cathode follower (e.g., Marshall). The circuit schematic of
a testable guitar tube preamp is shown in Figure 1 and the
values for circuit elements are listed in Table 1.Thevalues
were chosen as typical values for tube circuits. This preamp
consists of four common-cathode triode amplifiers. This
circuit schematic is similar to circuit schematics that are used
in some real “Hi Gain” guitar amplifiers (e.g., Mesa Boogie
or Engl). Only the values of the circuit elements can have
differing values, some of the common-cathode amplifiers
canhaveacathodecapacitortogainthehighfrequencies
and a potentiometer “Gain” with a capacitor connected
between terminals of the potentiometer are used instead of
resistors R
2
and R
g2
(see Figure 1). However, the frequency
dependence of the “Gain” parameter caused by the capacitor
can be simulated by a linear filter.
In order to simulate the preamp from Figure 1,an
analysis of the circuit must be done. The circuit equations
can be obtained using nodal analysis. In this case, Kirchhoff
¨
ı
Current Law (KCL) is used. After obtaining circuit equations,
a discretization of differential equations is realized. The
backward Euler method with step of a sampling period was
used. Naturally, a different method for discretization can be
used, however, this method was chosen due to its simplicity.
More information about obtaining the equations can be
Table 1: Values for circuit elements for Figure 1.
R
1
R
g1
R
k1
R
p1
R
2
R
g2
68 kΩ 1MΩ 2.7 kΩ 100 kΩ 470 kΩ 1MΩ
R
k2
R
p2
R
3
R
g3
R
k3
R
p3
1.8 kΩ 100 kΩ 470 kΩ 470 kΩ 1.8 kΩ 100 kΩ
R
4
R
g4
R
k4
R
p4
R
L
—
470 kΩ 470 kΩ 1.8 kΩ 100 kΩ 4MΩ —
C
1
C
2
C
3
C
4
V
ss
—
1 µF 22nF22nF22nF400V —
found in [13]. The final circuit equations describing the
circuit in Figure 1 are
0
=
V
in
− V
g1
G
1
− V
g1
G
g1
− i
g1
,
0
= V
c1m
− V
c1
−
V
k
G
k
− i
p1
− i
g1
C
1
f
s
,
0
=
V
ss
− V
p1
G
p1
−
V
2
− V
g2
G
2
− i
p1
,
0
= V
c2m
− V
c2
−
V
2
− V
g2
G
2
C
2
f
s
,
0
=
V
2
− V
g2
G
2
− V
g2
G
g2
− i
g2
,
0
= V
k2
G
k2
− i
g2
− i
p2
,
0
=
V
ss
− V
p2
G
p2
−
V
3
− V
g3
G
3
− i
p2
,
0
= V
c3m
− V
c3
−
V
3
− V
g3
G
3
C
3
f
s
,
0
=
V
3
− V
g3
G
3
− V
g3
G
g3
− i
g3
,
0
= V
k3
G
k3
− i
g3
− i
p3
,
0
=
V
ss
− V
p3
G
p3
−
V
4
− V
g4
G
4
− i
p3
,
0
= V
c4m
− V
c4
−
V
4
− V
g4
G
4
C
4
f
s
,
0
=
V
4
− V
g4
G
4
− V
g4
G
g4
− i
g4
,
0
= V
k4
G
k4
− i
g4
− i
p4
,
0
=
V
ss
− V
p4
G
p4
− V
p4
G
L
− i
p4
,
(1)
where i
g
is the grid current function i
g
(U
g
− U
k
)andi
a
is the
plate current function i
a
(U
g
− U
k
, U
a
− U
k
). The symbol G
represents the conductance of the resistors from the circuit
schematic in Figure 1, f
s
is the sampling frequency, V
ss
is the
power supply, and V
in
is the input voltage. Voltages V
c1m
,
V
c2m
, V
c3m
,andV
c4m
are the voltages on the capacitors in
the previous signal sampling period. Equations (1)have15
unknown voltage variables (denoted in Figure 1); 1st, 5th,
9th, 13th equations describe grid nodes of all tubes four
EURASIP Journal on Advances in Sig nal Processing 3
R
p1
V
g1
V
p1
V
k1
C
1
C
4
C
2
R
k1
V
2
R
2
V
g2
R
g2
R
g1
R
p2
V
p2
V
p3
C
3
R
g3
V
g3
R
3
V
3
R
k3
V
k3
R
k2
V
k2
R
4
V
4
R
g4
V
g4
R
p4
R
p3
V
p4
R
k4
V
k4
R
L
Gain
potentiometer
R
1
V
in
V
ss
Figure 1: A guitar tube preamp circuit schematic.
tubes; 2nd, 6th, 10th, and 14th equations are cathode nodes
and 3rd, 7th, 11th, 15th equations are anode nodes. Koren’s
nonlinear model of a triode has been used in this paper [15].
The triode plate current is given by
i
a
=
E
E
x
1
K
g1
1+sgn
(
E
1
)
,(2)
where
E
1
=
U
ak
K
p
log
⎛
⎝
1+exp
⎛
⎝
K
p
⎛
⎝
1
µ
+
U
gk
K
vb
+ U
2
ak
⎞
⎠
⎞
⎠
⎞
⎠
.
(3)
Parameters µ, E
x
, K
g1
, K
g2
, K
p
,andK
vb
,areavailablein[15],
U
gk
is the grid-to-cathode voltage and U
ak
is the plate-to-
cathode voltage. The grid current is not specified in [15],
and therefore the grid model was adopted from the Microcap
simulator [16]. The grid current is
i
g
=
⎧
⎪
⎨
⎪
⎩
g
cf
u
gk
− g
co
3/2
, u
gk
≥ g
co
,
0, u
gk
<g
co
,
(4)
where g
cf
= 1 · 10
−5
and g
co
=−0.2. Frequency properties
of the tube (e.g., Miller capacitance) are not considered in
the simulation due to simplicity of tube model. However,
it influences accuracy of the simulation, because the Miller
capacitance makes a low-pass filter at the input of the tube.
[17]
Generally, a system of nonlinear equation (1 )issolved
using the Newton-Raphson method
x
i+1
n
= x
i
n
− J
−1
x
i
n
F
x
i
n
,(5)
where x
i
n
is a vector of unknown voltages, J(x
i
n
)isthe
Jacobian matrix, and F(x
i
n
)isthefunctiongivenby(1), i
denotes iteration index and n is the time index.
However, if it is a real-time simulation, computational
demand has to be investigated. According to (1), the function
F(x
i
n
) involves four grid functions (4), four plate functions
(2), 47 add operations, and 3 1 multiply operations if the
sampling frequency f
s
is substituted with the sample period
T
s
= 1/f
s
and values of capacitors are substituted with
the reciprocal values. Considering the computational cost
of nonlinear device model functions (2)and(4)asc
p
and
c
g
respectively, the total cost of the function F(x
i
n
)isc
f
=
78 + 4c
p
+4c
g
operations. The J a cobian matrix J(x
i
n
)contains
partial first-order derivation of t he function F(x
i
n
). Since
the function F(x
i
n
) has not been a continuous function,
the derivations are computed for instance using the finite
difference formula
f
(
x
)
=
1
h
f
(
x + h
)
− f
(
x
)
(6)
with a step h that consists of two function calls F(x
i
n
)and
two add operations and one multiply operation. The total
cost of the Jacobian matrix computation is N +1function
calls resulting in (c
f
+3)N operations. When the Jacobian
matrix is established, its inversion matrix is computed. The
computational complexity depends on the chosen algorithm
of the matrix inversion. Generally, it is a O(N
3
)problem.
However, the LU decomposition offers a more efficient
implementation. Then, (5) is rewritten as
LU
= J
x
i
n
,(7)
Ly
= F
x
i
n
,(8)
UΔx
i
n
= y,(9)
x
i+1
n
= x
i
n
− Δx
i
n
. (10)
According to [18], the cost of (7) (Crout’s algorithm) is
(1/3)N
3
of inner loops containing one multiply and add
operation, the cost of (8)and(9)isN
2
multiply and add
operations. Thus, the total cost is c
LU
= (2/3)N
3
+4N
2
operations. Knowing Δx
i
n
,(10) can be solved, which requires
N add operations. The total cost of the New ton method is
then
c
nm
= i
c
f
+3
N + c
f
+
2
3
N
3
+4N
2
+ N
, (11)
where i is a number of iterations of the Newton method
and N is the number of circuit nodes. However, it must be
said that this number is theoretical. Neither of the algorithm
branches nor memory movements have been considered.
4 EURASIP Journal on Advances in Signal Processing
Triod e
amp. 1
Triod e
amp. 2
Triod e
amp. 2
Triod e
amp. 3
Triod e
amp. 3
Triod e
amp. 4
Figure 2: Preamp block decomposition using modified blockwise
method [13].
C
1
R
1
R
p1
V
g1
V
p1
V
k1
C
2
R
k1
V
2
R
2
V
g2
R
g2
R
p2
V
p2
R
k2
V
k2
R
L
R
g1
V
ss
V
in
Figure 3: Circuit schematic of the first block of the guitar preamp.
3. Simulation Using Modified
Blockwise Method
The simulated circuit c an also be divided into b locks. An
example of division into blocks using the modified blockwise
method [14]isshowninFigure2. The whole circuit from
Figure 1 is divided into three separate blocks containing two
tubes in this case. Two types of blocks are used. The first
one contains the common-cathodetubeamplifierwitha
cathode capacitor and is used as the first block in Figure 2.
The other one contains the common-cathode tube amplifier
without the cathode capacitor and is used as the second
and third block. Then, the simulation requires a solution
of three independent simpler blocks, and as a result the
computational complexity can be lower.
The circuit schematic of the first block is shown in
Figure 3. It consists o f two common-cathode tube amplifiers,
thefirsttubeamplifierisconnectedexactlyaccordingtothe
circuit schematic in Figure 1. The second tube amplifier is
similar to the second tube amplifier in 1, only the second
decoupling capacitor is not included and the load resistor
R
L
is connected directly to plate of the second tube. This
can be done, because the value of resistor R
L
and presence
of decoupling capacitor have very low influence on the first
preamp [14] because the output signal is obtained at the
output of the first amplifier and the second amplifier builds
only the load of the first amplifier. The value of resistor R
L
is
4MΩ and the others circuit elements values can be obtained
from Table 1. The output signal voltage is obtained from the
node V
2
.
The circuit is described by
0
=
V
in
− V
g1
G
1
− V
g1
G
g1
− i
g1
,
0
= V
c1m
− V
c1
−
V
k
G
k
− i
p1
− i
g1
C
1
f
s
,
0
=
V
ss
− V
p1
G
p1
−
V
2
− V
g2
G
2
− i
p1
,
0
= V
c2m
− V
c2
−
V
2
− V
g2
G
2
C
2
f
s
,
0
=
V
2
− V
g2
G
2
− V
g2
G
g2
− i
g2
,
0
= V
k2
G
k2
− i
g2
− i
p2
,
0
=
V
ss
− V
p2
G
p2
− V
p2
G
L
− i
p2
.
(12)
The function F(x
n
) involves 15 multiply operations and 22
add operations and four nonlinear function calls resulting
in c
f
= 37 + 2c
p
+2c
g
operations. The equation is solved
using the Newton method as well. Therefore, (11)canbe
used for t he determination of the computational complexity.
For given N
= 7, the total cost is
c
nm
= i
749 + 16
c
g
+ c
p
. (13)
Figure 4 shows the circuit schematic of the second and
third block. The circuit elements values can be derived from
Tab l e 1. The block contains t wo common-cathode tube
amplifiers without the cathode capacitor. The output signal
is obtained from the node V
2
.
The computational complexity is similar to the first
block. The y only differ in the function F(x
n
), because the
cathode capacitor in the first amplifier is missing (circuit
equations are not explicitly shown). Therefore, it has c
f
=
34 + 2c
p
+2c
g
operations in this case. The cost is then
c
nm
= i
728 + 16
c
g
+ c
p
. (14)
If the blocks are connected together, the total cost of the
solution of these blocks is
c
nm
= i
1
749 + 16
c
g
+ c
p
+
+
(
i
2
+ i
3
)
728 + 16
c
g
+ c
p
,
(15)
where i
1
, i
2
,andi
3
are numbers of iterations of the first,
second, and third block, respectively.
4. Simulation Using Modified Blockwise
Method and Approximation
Asystemofdifferential equations can be described by the
equation
0
= F
(
V
in
, V
c1
, V
c2
, ..., V
cM
)
,
(16)
where V
in
is an input voltage or an input signal value and
V
c1
, V
c2
, ..., V
cM
are voltages on capacitors. Thus, the system
EURASIP Journal on Advances in Sig nal Processing 5
R
1
R
p1
V
g1
V
p1
V
k1
C
2
R
k1
V
2
R
2
V
g2
R
g2
R
p2
V
p2
R
k2
V
k2
R
L
R
g1
V
in
V
ss
Figure 4: Circuit schematic of the second block of the guitar
preamp.
has N = M +1 inputs and also N outputs—an output voltage
or an output signal level and new voltages on the capacitors.
The system has a particular solution for a combination of the
inputs. The solution can be precomputed for certain combi-
nations of the input voltages and it must be approximated,
because the solution has to involve all combinations of the
input voltages [13]. The output signal value is computed on
the basis of the approximated solution. Then, the state of the
circuit (capacitor voltages) is actualized. The new capacitor
voltages are used as the inputs for the next sample period.
The approximation leads to the following set of equations:
V
n
out
= F
out
V
n
in
, V
n
c1
, V
n
c2
, ..., V
n
cM
,
V
n+1
uc1
= V
n
uc1
+ T
s
F
c1
V
n
in
, V
n
c1
, V
n
c2
, ..., V
n
cM
,
V
n+1
uc2
= V
n
uc2
+ T
s
F
c2
V
n
in
, V
n
c1
, V
n
c2
, ..., V
n
cM
,
.
.
.
V
n+1
ucM
= V
n
ucM
+ T
s
F
cN
V
n
in
, V
n
c1
, V
n
c2
, ..., V
n
cM
,
(17)
where functions F
out
, F
uc1
, F
uc2
, ..., F
ucM
are approximating
functions, T
s
is the sample period, and the superscript n
denotes time index. The function F
out
approximates directly
the output signal value and the functions F
uc1
, F
uc2
, ..., F
ucM
approximate changes of the capacitor voltage. The compu-
tational complexity depends on the number of accumula-
tion circuit elements (capacitors) N. The computation of
one output signal sample requires M,addoperations,M,
multiply operations and N, computations of approximating
functions. Therefore, the total computational complexity
markedly depends on the chosen approximation. Further-
more, the chosen approximation influences the accuracy of
the algorithm and also memory requirements.
(i) Linear Interpolation offers the fastest implementa-
tion. However, it requires many precomputed values
for smooth behavior. The linear interpolation is given
by
V
out
= V
i
out
1 − p
+ V
i+1
out
p
, (18)
where V
i
out
are precomputed output values, i is index
into a vector of fprecomputed output values and p is
a fractional part between neighbouring precomputed
values [19]. The linear interpolation consists of two
add operations, two multiply operations, index and
fractional part computation. The bilinear interpola-
tion has to be used as the approximating function
for the first-order differential system, because the
system has two inputs: input signal and the capacitor
voltage. The bilinear interpolation requires three lin-
ear interpolations (18). Generally, the N-dimensional
linear interpolation must be used, if t here are M
accumulation elements. It requires 2
N
− 1linear
interpolations.
(ii) Spline Interpolation offers the smooth behavior of an
approximating function. The spline interpolation is
given by
V
out
=
p
3
6
V
i−1
out
+
1+p
3
− 4p
3
6
V
i
out
+
2 − p
3
− 4
1 − p
3
6
V
i+1
out
+
1 − p
3
6
V
i+2
out
,
(19)
where p is the fractional part between neighbouring
precomputed values [19]. It consists of 10 add oper-
ations and 19 multiply operations, index and frac-
tional part computation. The N-dimensional spline
interpolation requires (4
N
−1)/3 spline interpolations
(19).
(iii) Cubic Spline Approximation also offers the smooth
behavior of approximating function but compared
to the spline interpolation, coefficients of cubic
polynomials are stored instead of the precomputed
values. The cubic polynomial is given by
V
i
out
= a
j
V
3
in
+ b
j
V
2
in
+ c
j
V
in
+ d
j
=
a
j
V
in
+ b
j
V
in
+ c
j
V
in
+ d
j
,
(20)
where j index is into a vector of polynomial coeffi-
cients [20]. It consists of three add operations and
three multiply operations. The combination of spline
and linear approximation can be used for the higher-
order system simulation, because the functions that
are being approximated have very similar shape
with different capacitor voltages V
c1
, V
c2
, ..., V
cN
,
as shown in simulations in [13]. The splines are
used for different input signal values and the linear
interpolation (18)fordifferent capacitor voltages.
Two spline approximations (20)andonelinear
interpolation (18) are required for the first-order
system (N
= 2). Generally, it requires 2
(N−1)
−1linear
interpolations and 2
(N−1)
spline approximations.
All approximations and interpolations work with indexes
into a vector of spline coefficients or precomputed values of
6 EURASIP Journal on Advances in Signal Processing
the solution of the system. It is necessary to find efficient
determination of the index, if it is real-time simulation. The
fastest way of determination of the index is computation
directly from the input values. The solution of the system is
precomputed for integer values of the inputs. Then the index
is obtained from
i
=V
in
(21)
and the fractional part is obtained from
p
= V
in
− i.
(22)
The system of (12) has three inputs (input voltage and
two input voltages on capacitors), and therefore M
= 2and
N
= 3 and three approximating functions are needed. The
solution was precomputed (er ror limit of Newton method
was 0.0001). The approximating functions F
out
, F
c1
,andF
c2
are plotted in F igure 5 for input voltage signal between −20
and 50 V and for capacitor voltages V
C 1
= 0V,5VandV
C 2
=
100 V, 200 V, 300 V. Naturally, the capacitor voltages can have
any value between 0 V and the power supply voltage.
The individual functions have very similar shapes with
different capacitor voltages. Therefore, splines are used for
the approximation with constant capacitor voltages and
different input voltage V
in
and then, computed spline
coefficients are stored in look-up table in a row r that was
computed as a linear function of the input and c apacitor
voltages
r
= V
in
V
c1
steps
V
c2
steps
+ V
c1
V
c2
steps
+ V
c2
.
(23)
Then, the spline coefficients are computed for different
capacitor voltage values. Subsequently, linear interpolation
between spline curves is used in order to get the final values.
It was experimentally found that the coefficient computation
can be made for two values of the capacitor voltage V
C 1
(minimal value 0 V and maximal possible value that depends
on values of resistors in the circuit). The step of the capacitor
voltage V
C 2
was 5 V between 0 V and the power supply
voltage 400 V. Other capacitor voltages are interpolated. The
input voltage grid was
±200 V w ith a step of 1 V (see Table 2).
The input voltages can be of course lower than 1 V, but in
this range the approximated function is almost linear, and
therefore it can be approximated by the spline with low error.
The maximal chosen deviation between numerical solution
and approximation was 0.1 V. Index into the table of spline
coefficients is dependent on the input voltage and capacitor
voltages and a number of rows r of the table can be obtained
from
n
r
= V
in
steps
V
c1
steps
V
c2
steps
(24)
and in this case, it is 64000 rows. The size is 2 MB per table, if
double precision floating point numbers are used. The total
size of all tables is 6 MB. The final equation for the simulation
of this block are
V
n
out
= F
out
V
n
in
, V
n
c1
, V
n
c2
,
V
n+1
uc1
= V
n
uc1
+ T
s
F
c1
V
n
in
, V
n
c1
, V
n
c2
V
n+1
uc2
= V
n
uc2
+ T
s
F
c2
V
n
in
, V
n
c1
, V
n
c2
.
, (25)
Table 2: Lookup table for simulation of the first block (size 2 MB).
Variable Min. Max. Step
V
in
[V] −200 200 1
V
C1
[V] 0 5 5
V
C2
[V] 0 400 5
Table 3: Look-up table for s imulation of the second and third block
(size 1 MB).
Variable Min. Max. Step
V
in
[V] −200 200 1
V
C1
[V] 0 400 5
Table 4: Computational complexity comparison of simulations
based on the New ton method—number of operations.
Simulation
type
One
iteration
Maximal
iteration
Average
iteration
Whole 5. 48 × 10
3
5.48 × 10
5
1.53 × 10
4
By blocks 2.97 × 10
3
1.39 × 10
4
6.89 × 10
3
The circuit schematic from Figure 4 has only two
inputs—input voltage V
in
and capacitor voltage V
C 1
.There-
fore, only two approximating functions (F
out
and F
c1
)are
needed (see Figure 6). They were approximated using the
same technique as the functions in the previous circuit.
The input voltage grid was
±200 V with a step of 1 V and
capacitor voltage V
c1
grid was between 0 V and the power
supply voltage with a step of 5 V (see Table 3). The total size
of both tables is 2 MB in this case. The final equations for the
simulation of this block are
V
n
out
= F
out
V
n
in
, V
n
c1
,
V
n+1
uc1
= V
n
uc1
+ T
s
F
c1
V
n
in
, V
n
c1
.
(26)
The final simulation equations (25), (26)arequite
simple. This is the biggest advantage when comparing with
other methods for real time simulation, for example, the
state space method, which requires matrix operations and
also nonlinear function precomputation stored N-D lookup
table.
5. Computational Complexity
Theproposedalgorithmswerecomparedwithregardtothe
computational complexity. For this purpose, the functions
(2)and(4) of the nonlinear device model were tabulated and
interpolated using the linear interpolation. As a result, the
cost c
g
is two add operation, and two multiply operations,
the cost c
p
is six add operations and six multiply operations.
Tab l e 4 shows a number of operations required for the
simulations based on the Newton method. Since the New ton
method is an iterative process, the number of operations
was investigated for one iteration, for the average number
of iterations and the maximal number of iterations per
sample as well. However, the average and maximal number of
iterations depend on the type of the input signal. Therefore,
EURASIP Journal on Advances in Sig nal Processing 7
−20 −100 1020304050
−300
−200
−100
0
100
V
2
(V)
V
in
(V)
(a)
F
c1
(Vs
−1
)
−20 −100 1020304050
0
−5000
5000
V
in
(V)
(b)
F
c2
(Vs
−1
)
−20 −100 10203040
50
15
10
5
0
−5
×10
4
V
in
(V)
V
c1,2
: 0 V, 300 V
V
c1,2
: 5 V, 300 V
V
c1,2
:5V,200V
V
c1,2
:0V,100V
V
c1,2
:5V,100V
V
c1,2
: 0 V, 200 V
(c)
Figure 5: Approximating functions for the simulation of the system (12)—output function (a), capacitor C
1
up-date (b) and capacitor C
2
update (c) functions.
−20 −100 1020304050
−400
−200
0
200
400
V
in
(V)
V
out
(V)
(a)
−2
0
2
4
6
8
×10
4
−20 −100 1020304050
F
c1
(Vs
−1
)
V
in
(V)
V
c1
:0V
V
c1
: 100 V
V
c1
: 200 V
V
c1
: 300 V
V
c1
: 400 V
(b)
Figure 6: Approximating functions for the simulation of the system (12)—output function (a) and capacitor C
2
update function (b).
the algorithms were tested with an E-chord guitar riff
with maximal amplitude around 200 mV. The whole circuit
simulation required 2.79 iterations on average and 100
iterations at the most. In the case of the simulation using
block decomposition, the average numbers of iterations were
i
1
= 2.1294, i
2
= 2.4149, i
3
= 2.6806 and maximal numbers
of iterations were i
1
= 4, i
2
= 5, i
3
= 100 for each block,
respectively. The number of iteration of individual block
differs because each block processes a different signal. The
maximum number of iteration was 100 and the error limit of
the Newton method was 1
× 10
−5
. The number of iteration
was computed from the whole sig nal (5 s, 240
×10
3
samples).
The computational complexity of algorithms b ased on
approximations is shown in Table 5. There are available
results for the whole preamp simulation as well as for the
blockwise simulation. The numbers were computed from
(25)and(26)wheredifferent types of approximation of
appropriate order N from Section 4 were used. Similarly, the
whole system can be approximated by order of approxima-
tion N
= 5, because the whole circuit contains 4 capacitors.
8 EURASIP Journal on Advances in Signal Processing
However, the whole circuit simulation was not implemented
due to complex approximating functions and also the look-
up table size would be huge.
As the results available in Tables 4 and 5 have shown,
the algorithms based on approximation offer constant
computational complexity, which is also much lower than
at the algorithms based on the Newton method. The linear
interpolation has the lowest computational complexity.
However, due to higher memory demands it is not suitable
and therefore the spline approximation was chosen as the
best method.
6. Simulation Results
The algorithm was implemented in C++ language as the
VST plug-in effect and then tested in realtime. It was tested
with a 2.66 GHz i7 Intel Mac with 4 GB RAM at a sampling
frequency of 48 kHz using an external audio interface M-
Audio Fast Track Pro with the ASIO buffer size 128 samples.
If no oversampling was used, the CPU load was around 3%.
To reduce aliasing distortion, 4-x oversampling was imple-
mented. Poly phase FIR interpolation and decimation was
used. The CPU load with 4-x oversampling was around 6%.
The proposed algorithm was then tested with different input
signals including a sinusoid signal at different frequencies
and amplitudes (including frequencies close to the Nyquist
frequency), logarithmic sweep signal (see Figure 7)andalso
a real guitar signal. All the performed simulations were stable
even if no oversampling was used and the amplitude of the
testing signal was around hundreds of volts at the input of the
third block. Comparison between the simulation of whole
preamp using the Newton method and the simulation based
on the spline approx imation is plot ted in Figure 8.Time
difference signals are normalized to the maximal value of the
output signal.
The error values, such as maximal and average error, are
also expressed in Table 6. The maximal error is around 2 V.
An amplification of the preamp is approximately 1. 84
× 10
4
and measured level of noise at t he output of the preamp
without connected guitar is approximately 4.7 V
pp
and with
connected guitar it is approximately 32 V
pp
(these high values
are caused by extremely high amplification, normally, the
amplification is lower). Therefore the error can be masked. In
places with the maximal error, the New ton method reached
the maximal number of iterations (100), and therefore the
error is caused partly by the approximation and partly by the
Newton method.
The c ircuit was also simulated with a reduced power
supply voltage to 261 V and the results were compared to
a real home-made guitar preamp connected according to
the circuit schematic in Figure 1 with the reduced power
supply. Firstly, the simulations using Newton method and
approximations were compared in Figure 9.Theerrorwas
approximately the same as in Figure 8—the maximum
error increased from 2 V to 3.5 V but t he average error
decreased from 6.20
× 10
−3
to 4.50 × 10
−3
V. T h e re l a t iv e
error in Figure 9 is higher due to lower power supply.
The comparison between the output of real circuit and its
Table 5: Computational complexity comparison of simulations
based on approximations—number of operations.
Simulation
type
Linear
interpretation
Spline
Interpertation
Spline
Approx
Whole 9. 38 × 10
2
5.28 × 10
4
9.53 × 10
2
By blocks 2.06 × 10
2
2.58 × 10
3
2.51 × 10
2
Table 6: Errors for s imulation from Figure 8.Theplatevoltage
signal errors are displayed.
— V
p1
V
p2
Max [V] 6.27 × 10
−4
2.38 × 10
−1
Mean [V] 1.13 × 10
−5
5.70 × 10
−3
var [V] 1.05 × 10
−7
2.54 × 10
−4
— V
p3
V
p4
Max [V] 2.12 × 10
−1
1.99
Mean [V] 1.01
× 10
−2
6.20 × 10
−3
var [V] 1.07 × 10
−3
0.13 × 10
−3
Table 7: Harmonics comparison from Figure 11.Themagnitudes
are related to the first harmonic.
— 23456
Meas. [dB] −35.1 −10.0 −36.1 −14.7 −37.7
Sim. [dB]
−27.4 −9.8 −28.3 −14.7 −29.5
Diff. [dB] 7.6 0.2 7.7 0.1 8.2
digital simulation was made using sinusoid signal because a
harmonic signal generator was used as a signal source for
the guitar preamp. The output signal was recorded using
soundcard. The input harmonic signal had an amplitude of
150 mV and a frequency of 1 kHz. Measured and simulated
time-domain signals are shown in Figure 10.Mostofthe
errors occur on transients, but the testing preamp was
extremely noisy, and therefore it is very hard to determine
the deviation of the simulation. The spectrum of the signals is
shown in Figure 11. The rectangular window with a length of
a hundredfold of the signal period (48 samples at a sampling
frequency of 48 kHz) was used to minimize spectrum leak-
age. The measured preamp was homemade and it was not
shielded. Therefore, intermodulation distortion components
occur in measured spectrum. Table 7 shows magnitudes
of the higher harmonic components r elated to the first
harmonic. The error between measurement and simulation
is shown as well. The odd harmonics were almost the
same, but the even harmonic were higher in the simulation.
However, this deviation can be caused by the tube model,
because the same simulation results were obtained using the
numerical solution of the whole circuit. The general model
of a 12ax7 tube was used in the simulation but the real tubes
have different properties—there are many different types
of 12ax7 tube and also the same type can differ because
of a manufacturer’s tolerance. In order to get an accurate
simulation, the general tube model must be tuned according
to the used tubes. However, it requires a measurement of the
transfer functions of the used tubes.
EURASIP Journal on Advances in Sig nal Processing 9
t (s)
f (Hz)
0 0.5 1 1.5 2 2.5 3
100
1000
10000
20000
(a)
t (s)
f (Hz)
0 0.5
1
1.5 2
2.5
3
100
1000
10000
20000
(b)
t (s)
f (Hz)
0 0.5
1
1.5 2
2.5
3
100
1000
10000
20000
(c)
t (s)
f (Hz)
0 0.5
1
1.5 2
2.5
3
100
1000
10000
20000
(d)
Figure 7: Simulation results for a logarithmic sweep signal. The plate voltage sig nals p
1
, p
2
, p
3
,andp
4
are displayed. The 32-x oversampling
was used to reduce aliasing .
0 500 1000
1500
t (ms)
0
0.005
0.01
0.01
0.02
0
U
p1
error (V)
U
p1
error (%)
(a)
0 500 1000 1500
t (ms)
U
p2
error (V)
0.2
0.4
0
0
0.05
0.1
U
p2
error (%)
(b)
0
0 500 1000 1500
t (ms)
0.05
0.1
0.2
0.4
0
U
p3
error (V)
U
p3
error (%)
(c)
0 500 1000 1500
t (ms)
0
0
2
U
p4
error (V)
0.5
U
p4
error (%)
(d)
Figure 8: Comparison between simulation results using numerical solution and using approximations for a part of a real guitar riff.Only
the error signals are displayed.
024681012141618
0
0.005
0.01
0
0.02
0.04
t (ms)
U
p1
error (%)
×10
3
U
p1
error (V)
(a)
0
0.01
0.02
0.03
0.04
0
0.05
0.1
0.15
0.2
U
p2
error (%)
0 2 4 6 8 10 12 14 16 18
t (ms)
×10
3
U
p2
error (V)
(b)
0
0.05
0.1
0
0.2
0.4
U
p3
error (%)
024681012141618
t (ms)
×10
3
U
p3
error (V)
(c)
0
0.5
1
0
2
4
U
p4
error (%)
0 2 4 6 8 10 12 14 16 18
t (ms)
×10
3
U
p4
error (V)
(d)
Figure 9: Comparison between simulation results using numerical solution and using approximations for the reduced power supply voltage.
10 EURASIP Journal on Advances in Signal Processing
0 0.002 0.004 0.006 0.008 0.01
−200
−100
0
100
200
t (ms)
U
out
(V)
(a)
t (ms)
U
out
(V)
0.9 1 1.1 1.2 1.3 1.4
×10
−3
100
110
120
130
140
150
Measured
Simulated
(b)
Figure 10: Comparison between measured and simulated preamp. The input voltage was sinewave signal with an amplitude of 150 mV and
afrequencyof1kHz.
0 0.5 1 1.5 2 2.5
×10
4
f (Hz)
Measured
0
20
40
M (dB)
(a)
Simulated by blocks
0 0.5 1 1.5 2 2.5
×10
4
f (Hz)
0
20
40
M (dB)
(b)
Simulated
0 0.5 1 1.5 2
2.5
×10
4
f (Hz)
0
20
40
M (dB)
(c)
Figure 11: Spectrum comparison between measured and simulated preamps.
7. Conclusion
In this paper, real-time simulation of a guitar tube amplifier
using approximations is proposed. The approximation of the
solution of differential equations offers sufficient accuracy
of the simulation while the computational cost is relatively
low. The approximations are used together with the modified
blockwise method that allows further reduction of the
computational complexity. The blockwise method has been
tested and it gives almost the same results as the simulation
of the whole circuit. The results of the amplifier simulation
were compared with the measurement of the real amplifier
and the results show that the quite good accuracy of the
simulation can be obtained. However, the compared signals
differs in even harmonics. This was probably caused by
the tube models that were used in simulation, because the
numerical solution of the whole circuit and simulation using
approximation were almost the same. Different amplification
factor of the t ube model can cause the bias shift resulting in
different results.
The major advantage of the proposed algorithm is con-
stant computational complexity and also the computational
complexity is independent from the number of nonlinear
functions in the simulated circuit or from the number of
circuit nodes. It depends only on the number of accu-
mulation elements. However, this is also disadvantageous,
because the implementation of approximating functions is
quite complicated. Therefore, practically, the number of
EURASIP Journal on Advances in Signal Processing 11
accumulation elements is bounded. Nevertheless, this disad-
vantage is compensated for by using the blockwise method.
Compared to other methods, this method offers quite simple
implementation, because it is derived directly from circuit
equations. Thus, no transformations are necessary and the
implementation should be faster. However, comparison with
other methods has not been done yet and therefore this will
be the next work.
Acknowledgment
This paper was prepared within the framework of project
no. FR-TI1/495 of the Ministry of Industry and Trade of the
Czech Republic.
References
[1] J. Pakarinen and D. T. Yeh, “A review of digital techniques
for modeling vacuum-tube guitar amplifiers,” Computer Music
Journal, vol. 33, no. 2, pp. 85–100, 2009.
[2] D. T. Yeh, J. S. Abel, and J. O. Smith, “Simulation of the diode
limiter in guitar distortion circuits by numerical solution of
ordinary differential equations,” in Proceedings of the Digital
Audio Effects (DAFx ’07), pp. 197–204, Bordeaux, France,
September 2007.
[3] D. T. Yeh, J. S. Abel, and J. O. Smith, “Simplified, physically-
informed models of distortion and overdrive guitar effects
pedal,” in Proceedings of the Digital A udio Effects (DAFx ’07),
pp. 189–196, Bordeaux, France, September 2007.
[4] A.Novak,L.Simon,andP.Lotton,“Analysis,synthesis,and
classification of nonlinear systems using synchronized swept-
sine method for audio effects,” EURASIP Journal on Advances
in Signal Processing, vol. 2010, Article ID 793816, 8 pages,
2010.
[5] J. Pakarinen, M. Tikander, and M. Karjalainen, “Wave digital
modeling of the output chain of a vacuum-tube amplifier,”
in Proceedings of the International Conference on Digital Audio
Effects (DAFx ’09), pp. 1–4, Como, Italy, September 2009.
[6] M. Karjalainen and J. Pakarinen, “Wave digital simulation of
a vacuum-tube amplifier,” in Proceedings of the IEEE Inter-
national Conference on Acoustics, Speech and Signal Processing
(ICASSP ’06), pp. 153–156, Toulouse, France, May 2006.
[7] J. Pakarinen and M. Karjalainen, “Enhanced wave digital
triode model for real-time tube amplifier emulation,” IEEE
Transactions on Audio, Speech and Language Processing,vol.18,
no. 4, pp. 738–746, 2010.
[8] D. T. Yeh and J. O. Smith, “Simulating guitar distortion circuits
using wave digital and nonlinear state-space formulations,” in
Proceedings of the Digital Audio Effects (DAFx ’08), pp. 19–26,
Espoo, Finland, September 2008.
[9] K. Dempwolf, M. Holters, and U. Z
¨
oLzer, “Disceetization
of parametric analog circuits for realtime simulations,” in
Proceedings of the 13th International Conference on Digital
Audio Effects (DAFx ’10), Graz, Austria, September 2010.
[10] D. T. Yeh, J. S. Abel, and J. O. Smith, “Automated physical
modeling of nonlinear audio circuits for real-time audio
effectspart I: theoretical development,” IEEE Transactions on
Audio, Speech and Language Processing, vol. 18, no. 4, pp. 728–
737, 2010.
[11] I. Cohen and T. Helie, “Real-time simulation of a guitar power
amplifier,” in Proceedings of the 13th International Conference
on Digital Audio Effects (DAFx ’10), Graz, Austria, September
2010.
[12] D. T. Yeh, J. S. Abel, A. Vladimirescu, and J. O. Smith, “Nu-
merical m ethods for simulation of guitar distortion circuits,”
Computer Music Journal, vol. 32, no. 2, pp. 23–42, 2008.
[13] J. Macak and J. Schimmel, “Real-time guitar tube amplifier
simulation using approximation of differential equations,” in
Proceedings of the 13th International Conference on Digital
Audio Effects (DAFx ’10), Graz, Austria, September 2010.
[14] J. Macak, “Modified blockwise method for simulation of
guitar tube amplifiers,” in Proceedings of 33nd International
Conference Telecommunications and Signal Processing (TSP
’10), pp. 1–4, Vienna, Austria, August 2010.
[15] N. K oren, “Improved vacuum tube models for SPICE sim-
ulations,” 2003, http://www.normankoren.com/Audio/Tube-
modspice
article.html.
[16] Spectrum Software, Micro-Cap 10,SpectrumSoftware,Sun-
nyvale, Calif, USA, 2010.
[17] J. M. Miller, “Dependence of the input impedance of a three-
electrode vacuum tube upon the load in the plate circuit,”
in Scientific Papers of the Bureau of Standards, pp. 367–385,
Washington, DC, USA, September 1920.
[18] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P.
Flannery, Numerical Recipes in C, Cambridge University Press,
Cambridge, UK, 2nd edition, 1992.
[19] U. Z
¨
oLzer, DAFX—Digital Audio Effects, John Wiley & Sons,
New York, NY, USA, 1st edition, 2002.
[20] C. de Boor, A Practical Guide to Splines,Springer,NewYork,
NY, USA, 1st edition, 2001.
