Impulse Response Free
Leoma Cianchetti
In signal processing and control theory, the impulse response, or impulse response function (IRF), of a dynamic system is its output when presented with a brief input signal, called an impulse (δ(t)). More generally, an impulse response is the reaction of any dynamic system in response to some external change. In both cases, the impulse response describes the reaction of the system as a function of time (or possibly as a function of some other independent variable that parameterizes the dynamic behavior of the system).
Since the impulse function contains all frequencies (see the Fourier transform of the Dirac delta function, showing infinite frequency bandwidth that the Dirac delta function has), the impulse response defines the response of a linear time-invariant system for all frequencies.
Mathematically, how the impulse is described depends on whether the system is modeled in discrete or continuous time. The impulse can be modeled as a Dirac delta function for continuous-time systems, or as the discrete unit sample function for discrete-time systems. The Dirac delta represents the limiting case of a pulse made very short in time while maintaining its area or integral (thus giving an infinitely high peak). While this is impossible in any real system, it is a useful idealization. In Fourier analysis theory, such an impulse comprises equal portions of all possible excitation frequencies, which makes it a convenient test probe.
Any system in a large class known as linear, time-invariant (LTI) is completely characterized by its impulse response. That is, for any input, the output can be calculated in terms of the input and the impulse response. (See LTI system theory.) The impulse response of a linear transformation is the image of Dirac's delta function under the transformation, analogous to the fundamental solution of a partial differential operator.
It is usually easier to analyze systems using transfer functions as opposed to impulse responses. The transfer function is the Laplace transform of the impulse response. The Laplace transform of a system's output may be determined by the multiplication of the transfer function with the input's Laplace transform in the complex plane, also known as the frequency domain. An inverse Laplace transform of this result will yield the output in the time domain.
To determine an output directly in the time domain requires the convolution of the input with the impulse response. When the transfer function and the Laplace transform of the input are known, this convolution may be more complicated than the alternative of multiplying two functions in the frequency domain.
In practical systems, it is not possible to produce a perfect impulse to serve as input for testing; therefore, a brief pulse is sometimes used as an approximation of an impulse. Provided that the pulse is short enough compared to the impulse response, the result will be close to the true, theoretical, impulse response. In many systems, however, driving with a very short strong pulse may drive the system into a nonlinear regime, so instead the system is driven with a pseudo-random sequence, and the impulse response is computed from the input and output signals.[1]
An application that demonstrates this idea was the development of impulse response loudspeaker testing in the 1970s. Loudspeakers suffer from phase inaccuracy, a defect unlike other measured properties such as frequency response. Phase inaccuracy is caused by (slightly) delayed frequencies/octaves that are mainly the result of passive cross overs (especially higher order filters) but are also caused by resonance, energy storage in the cone, the internal volume, or the enclosure panels vibrating.[2] Measuring the impulse response, which is a direct plot of this "time-smearing," provided a tool for use in reducing resonances by the use of improved materials for cones and enclosures, as well as changes to the speaker crossover. The need to limit input amplitude to maintain the linearity of the system led to the use of inputs such as pseudo-random maximum length sequences, and to the use of computer processing to derive the impulse response.[3]
Impulse response analysis is a major facet of radar, ultrasound imaging, and many areas of digital signal processing. An interesting example would be broadband internet connections. DSL/Broadband services use adaptive equalisation techniques to help compensate for signal distortion and interference introduced by the copper phone lines used to deliver the service.
In control theory the impulse response is the response of a system to a Dirac delta input. This proves useful in the analysis of dynamic systems; the Laplace transform of the delta function is 1, so the impulse response is equivalent to the inverse Laplace transform of the system's transfer function.
In acoustic and audio applications, impulse responses enable the acoustic characteristics of a location, such as a concert hall, to be captured. Various packages are available containing impulse responses from specific locations, ranging from small rooms to large concert halls. These impulse responses can then be utilized in convolution reverb applications to enable the acoustic characteristics of a particular location to be applied to target audio.[4]
In economics, and especially in contemporary macroeconomic modeling, impulse response functions are used to describe how the economy reacts over time to exogenous impulses, which economists usually call shocks, and are often modeled in the context of a vector autoregression. Impulses that are often treated as exogenous from a macroeconomic point of view include changes in government spending, tax rates, and other fiscal policy parameters; changes in the monetary base or other monetary policy parameters; changes in productivity or other technological parameters; and changes in preferences, such as the degree of impatience. Impulse response functions describe the reaction of endogenous macroeconomic variables such as output, consumption, investment, and employment at the time of the shock and over subsequent points in time.[5][6] Recently, asymmetric impulse response functions have been suggested in the literature that separate the impact of a positive shock from a negative one.[7]
Many modern Cab Sim Pedals such as the TC Electronic Impulse and Two Notes Torpedo C.A.B. M+ feature IR loading, making using IRs easier than ever. You can simply carry all of your favourite presets on your pedalboard.
To put it simply, convolution reverb refers to the simulation of a reverb, echo or the sonic quality of a space using impulse responses. As described earlier, a short sound is played, with the response then being measured and recorded, then recreated using algorithms. Popular convolution reverb plugins include Logic Pro X Space Designer, Waves IR1 and Space by Avid.
As mentioned earlier, convolution reverb is the simulation of ambience using an impulse response. Algorithmic reverb is totally artificial, recreating the effect of ambience by simulating natural echoes, EQ, decay times and other elements.
Abstract. Projections of the response to anthropogenic emission scenarios, evaluation of some greenhouse gas metrics, and estimates of the social cost of carbon often require a simple model that links emissions of carbon dioxide (CO2) to atmospheric concentrations and global temperature changes. An essential requirement of such a model is to reproduce typical global surface temperature and atmospheric CO2 responses displayed by more complex Earth system models (ESMs) under a range of emission scenarios, as well as an ability to sample the range of ESM response in a transparent, accessible and reproducible form. Here we adapt the simple model of the Intergovernmental Panel on Climate Change 5th Assessment Report (IPCC AR5) to explicitly represent the state dependence of the CO2 airborne fraction. Our adapted model (FAIR) reproduces the range of behaviour shown in full and intermediate complexity ESMs under several idealised carbon pulse and exponential concentration increase experiments. We find that the inclusion of a linear increase in 100-year integrated airborne fraction with cumulative carbon uptake and global temperature change substantially improves the representation of the response of the climate system to CO2 on a range of timescales and under a range of experimental designs.
During the envlp sweeps, I see that the first simulation takes about 40 minutes to for spectre to complete (including calculattion/create impulse response bin files) while the following steps in the sweep take no more than couple of minutes (useing the models). But with no changes/updates of the schematic/N-ports/netlist, on the next sweep once again the first simulation takes about 40 minutes and all the bin files are re-generated on the first run.
If I understand your question correctly, I think the option to save the impulse response files spectre creates from an S-parameter N-port file already exists and may be selected either through the GUI or through options set in either your .cdsinit file or .cdsenv file.
IRs are fine technology and incorporate nicely into amp modelling, but they do what they do which is to capture sonic response characteristics so they can be applied to a given sound. So they are a bit static when compared to the dynamic real-time adjustments that can be made to the Helix modeled cabinets, mic and mic placements. This is why some folks refer to their searches for IRs as "going down the rabbit hole".
A typical IR for something like a Mesa 2x12 cabinet may consist of 30 or 40 different files. Each file has a different variation of that cabinet such as a that cabinet mic'd with an SM57 located on the cap of the speaker, another file with the SM57 2 inches away from the cap of the speaker out toward the cone, and so forth. For each variation of mic, or mic mixes, and placements there will be a different IR file. If you don't know what you prefer as far as mic's, mic mixes, and placements you can spend a LOT of time searching through these files to find what you like. If you know what you like it's relatively easy to choose an IR variation and dismiss the rest.
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