ACME is a Julia package for the simulation of electrical circuits, focusing on audio effect circuits. It allows to programmatically describe a circuit in terms of elements and connections between them and then automatically derive a model for the circuit. The model can then be run on varying input data.
ACME is based on the method described in M. Holters, U. Zölzer, "A Generalized Method for the Derivation of Non-Linear State-Space Models from Circuit Schematics".
To install ACME, start Julia and run:
This will download ACME and all of its dependencies.
We will demonstrate ACME by modeling a simple diode clipper. The first step is to load ACME:
Now we create all the necessary circuit elements:
j_in = voltagesource() r1 = resistor(1e3) c1 = capacitor(47e-9) d1 = diode(is=1e-15) d2 = diode(is=1.8e-15) j_out = voltageprobe()
voltagesource() sets up a voltage source as an input, i.e. the
voltage it sources will be specified when running the model. Alternatively, one
can instantiate a constant voltage source for say 9V with
capacitor calls take the resistance in ohm and the
capacitance in farad, respectively, as arguments. For the
diode, one may
specify the saturation current
is as done here and/or the emission
η. Finally, desired outputs are denoted by adding probes to the
circuit; in this case a
voltageprobe() will provide voltage as output.
Next we need a
Circuit instance to keep track of how the elements connect to
circ = Circuit()
Connections can be specified by naming element pins that are connected:
connect!(circ, j_in[:+], r1)
This connects the positive output of the input voltage source with pin 1 of the resistor. Alternatively, one can introduce named nets to which element pins connect. This may increase readability for nets with many connected elements, like supply voltages. Here, we use it for the ground net where we connect the negative side of the input voltage:
connect!(circ, j_in[:-], :gnd)
One can also connect multiple pins at once:
connect!(circ, r1, c1, d1[:+], d2[:-], j_out[:+]) connect!(circ, :gnd, c1, d1[:-], d2[:+], j_out[:-])
Now that all connections have been set up, we need to turn the circuit description into a model. This could hardly be any easier:
model = DiscreteModel(circ, 1./44100)
The second argument specifies the sampling interval, the reciprocal of the sampling rate, here assumed to be the typical 44100 Hz.
Now we can process some input data. It has to be provided as a matrix with one row per input (just one in the example) and one column per sample. So for a sinusoid at 1 kHz lasting one second, we do
y = run!(model, sin(2π*1000/44100*(0:44099).'))
y now likewise is a matrix with one row for the one probe we have
added to the circuit and one column per sample.
In the likely event that you would like to process real audio data, take a look at the WAV package for reading writing WAV files.
Note that the solver used to solve the non-linear equation when running the model saves solutions to use as starting points in the future. Model execution will therefore become faster after an initial learning phase. Nevertheless, ACME is at present more geared towards computing all the model matrices than to actually running the model. More complex circuits may run intolerably slow or fail to run altogether.
There is some documentation
available for how
to use ACME. Additionally, you can take a look at the examples that can be found
examples directory below
If you would like to extend and improve ACME, that's great! But unfortunately, there is no developer documentation as of now, so you will to delve into the source code to figure out how things work, or try to ask on gitter.
28 days ago