Convolution reverb is said to be the go-to tool for realistic artificial reverberation. But what needs to be true so it’s really authentic?
MConvolutionEZ MConvolutionEZ is an easy-to-use highly optimized convolution reverb. Comes with lots of impulse responses for rooms, halls, plates, guitar cabinets, effects. Although convolution is often associated with high-end reverb processing, this technology makes many other new sounds available to you once you understand how it works. Convolution or 'sampling' reverbs are now extremely popular, and it's not hard to see why. The Convolution Reverb plug-in can be quite intensive for memory and CPU usage. It is a good idea to budget the necessary memory and CPU early on in the design. Make sure that you are familiar with optimization parameters, such as impulse response threshold, truncation, and downmix, because these parameters can all help ensure that the. Key to balancing a reverb mix is the ability to change the relative level of reflections and tail. Seventh Heaven has individual reverb engines and a precise early/late reverb dial so you can craft the perfect reverb. Very Low Frequency Reverb. The low frequency reverb in an M7 is a work of art in itself.
Convolution reverbs are reverb effects which use impulse responses (IRs) to simulate an acoustic space. These are the best freeware convolution reverb effects in VST plugin format for digital audio workstations on PC and Mac.
At first sight, convolution reverb technology looks quite fool-proof. In theory, you can practically sample the exact behavior of an acoustic space or reverb algorithm. Or can you? Let’s look a bit closer at how it works.
Convolution Reverb Basics
The general concept behind convolution reverb is to capture the impulse response of an acoustic space. The impulse response is the signal you get if you feed any linear and time-invariant (“LTI”) system with a perfect impulse. In digital signal processing, such an impulse is a single full-scale sample and only zeros otherwise. Please understand that I’d rather not confuse you with the definition of such an impulse in the analog world right now.
If you know the impulse response, you know the exact behavior of an LTI system. At least in the digital world it is obvious why that is. Every digital recording is just a series of many impulses, and an LTI system responds each one in the same way: by outputting a scaled version of it’s impulse response. It’s thus trivial to reproduce this behavior artificially using digital signal processing. It gets a little harder if you want to do it in real-time, latency free and with a reasonable amount of CPU. But the basic technique is trivial.
A practical problem with this approach is also immediately obvious: an impulse response is just a static measurement. Your chances of changing some properties like reverberation time or source position after the fact are very limited, compared to other artificial reverb techniques. To realistically place orchestral instruments in a concert hall, you need lots and lots of impulse response measurements. Also, things like the sound source and microphones used for impulse response measurement affect the outcome. Especially their directivity. However, that’s not what I want to focus on today.
Let’s revisit one sentence from above again: If you know the impulse response, you know the exact behavior of an LTI system. I highlighted the important parts here, where two questions arise:
- Does the assumption of an LTI system hold for reverb?
- Can we exactly determine the impulse response?
Today we’ll focus on the first question. Next week we’ll look at the second one.
Is Reverb Linear and Time-Invariant?
For now I want to focus on the original meaning of a reverb: reverberation in an acoustic space such as a room, a cathedral or whatever place in the real world comes to your mind. It is generally assumed that these environments behave – acoustically – in a linear and time-invariant way.
Linearity means that it doesn’t matter if you reverberate a mixture of several signals together, or reverberate the individual signals one after another and then mix the results. The result will be the same. Similarly, the reverberation is the same – relatively – no matter how loud the signals are.
Time-invariance means that at any instance in time, the result of feeding a signal through a system will be the same, thus characteristics don’t change over time.
So is this true for an acoustic environment?
Generally, sound is rapidly moving air resulting in small local changes in air pressure, which in turn results in other moving air molecules and so on. To get from moving air and air pressure to a set of wave equations describing a linear sound field, some assumptions and prerequisites need to be met:
- Magnitude of sound pressure changes are small compared to the static air pressure.
- Consequently, static air pressure must be constant.
- The sound field must be irrotational, which means air doesn’t move around in circles.
Only if these criteria are true can we assume the sound field to be linear.
Under Pressure
The first criterion is easily met as long as we are talking about non-lethal sound pressure levels on the peak of mount everest and below. At sea level, 120 dB SPL is still around 1/5000th of the static air pressure. Lucky us!
Speaking of static air pressure: we know from the weather report that it is not constant. So there’s that. But at least it changes only very slowly. Nevertheless, we have a problem here, because the Wiener Musikverein might sound different depending on the current weather. Anyone in for a blind listening study?
Similarly, the air in the acoustic space should be generally at rest. Larger air movement will cause drastic changes in sound radiation properties. At every open air concert, when you’re a few 100 meters away from the stage, you’ll notice that during gusts of wind the sound becomes louder or softer for a short time, and high frequencies are damped more or less, depending on wind direction.
So it looks like we already have a problem here. But what about this irrotational thing?
You Spin Me Right ‘Round
In the free field, a sound field with only air and no other obstacles or boundaries, the sound field is indeed irrotational. The same is still true if boundaries and obstacles are present, but large compared to the wavelengths of sound.
In real-life situations however, this is not the case. There are objects, walls and edges of all sizes (which is actually a good thing – perceptually). What happens is that when air moves in the vicinity of sharp edges, it often creates little whirlwinds, which invalidate our irrotationality criterion. You can notice the problem for example with closed loudspeaker designs when the enclosure is not perfectly airtight. Bass frequencies can then sometimes produce subtle wind noises (some kind of pumping white noise).
The same goes for easily moving objects that collide with other objects. A typical example are door panels in your car that start rattling when the subwoofer goes to work.
Of course, these rattling and blowing issues are extreme examples that you don’t want anyway, so there’s no point in whining about the fact that impulse responses cannot accurately capture it. But still there are very subtle nonlinear effects in real acoustic spaces, especially with very long reverb times. The result is a certain amount of chaos and time-variance in real rooms that will get lost in impulse response measurements.
Some Perspective
Unfortunately I don’t know about any studies to quantify how strong the influence of these small chaotic ingredients actually is to a realistic reverb tail. The experimental design would be very difficult anyway. And then it’s still unclear how important this would be perceptually.
The thing is, human perception doesn’t care so much about the finest details of reverberation. In fact, it goes to some lengths to ignore most of this information. We recognize a space by very basic characteristics such as the sequence and coloration of early reflections and the frequency-dependent buildup and decay of the reverb tail. Very fine differences in the exact reflections don’t matter much once they are sufficiently dense.
In non-huge spaces, the rather academic effects described above likely don’t build up sufficiently before the reverb tail decays into inaudibility. As reverberation is all about small effects that are repeated over and over in chaotic feedback loops, it would take some time for such small effects to become dominant. The additional decorrelation however can have an impact on perceived spaciousness and audibility of late reverberation in conjunction with long sustained tones (more on that next week!).
Conclusions
Although acoustic reverberation can be largely considered LTI, there are sources for time-variance and nonlinear effects. The larger and more complex the space, the more these may (subtly) matter. Nevertheless, for the most important audible features, convolution reverb is a great way to convincingly simulate real acoustic spaces. And it’s no doubt the most exact and realistic method available to us.
Next week we’ll start out with something we omitted so far: algorithmic reverbs. And afterwards we’ll have a look at some methods to actually measure impulse responses. And at how the results are affected by noise, time-variance and other disturbances.
Convolution Reverb
Head right on to Part 2!
In audio signal processing, convolution reverb is a process used for digitally simulating the reverberation of a physical or virtual space through the use of software profiles; a piece of software (or algorithm) that creates a simulation of an audio environment. It is based on the mathematical convolution operation, and uses a pre-recorded audio sample of the impulse response of the space being modeled. To apply the reverberation effect, the impulse-response recording is first stored in a digital signal-processing system. This is then convolved with the incoming audio signal to be processed.
Creation of impulse responses[edit]
Directly recording impulses[edit]
An impulse response is a recording of the reverberation that is caused by an acoustic space when an ideal impulse is played. However, an ideal impulse is a mathematical construct, and cannot exist in reality, as it would have to be infinitesimally narrow in time. Therefore, approximations have to be used: the sound of an electric spark, starter pistol shot or the bursting of a balloon, for instance. A recording of this approximated ideal impulse may be used directly as an impulse response. Techniques involving starter pistols and balloons are sometimes referred to as transient methods, and the response is contained at the beginning of the recording in an impulse.
Sine sweep method[edit]
Another technique, referred to as the sine sweep method, covers the entire audible frequency range, which can result in a broader-range, and higher-quality, impulse response. This involves the use of a longer sound to excite a space (typically a sine sweep), which is then put through a process of deconvolution to produce an impulse response. This approach has the advantage that such sounds are less susceptible to distortion; however, it requires more sophisticated processing to produce a usable impulse response.
Maximum Length Sequences[edit]
A third approach involves using maximum-length sequences. This uses a constant-power signal instead of an impulse, so does not require as much dynamic range when recording.[1]
Construction based on transfer function[edit]
The transfer function (or frequency response) of a system can be measured using any sound that covers the frequency spectrum. For example, to sample the acoustic properties of a larger space such as a small church or cathedral, the space can simply be excited using white noise, with the result recorded both near the source, and somewhere else in the space.
The coefficients of a finite impulse response can then be generated as the inverse Fourier Transform of the cross-correlation of the output of the system with the auto-correlation of the input to the system. This is difficult in practice because such sequences are highly susceptible to distortion.[citation needed]
Applications[edit]
Real space simulation[edit]
The primary goal of a convolution reverb is to sample real spaces, in order to simulate the acoustics of the sampled space. A straightforward and simple mono example of capturing an impulse response would be to set up a microphone in a concert hall and to place the microphone in the centre of the auditorium. Next, produce a very brief pulse (often an electric spark) of sound, and record everything that the microphone picks up, which includes both the original sound and the response of the room to it. The recorded take would then be cleanly edited and loaded into the convolution processor. This convolution can be applied as part of a signal processing chain.
Machine simulation[edit]
It is also possible to sample the impulse response of a reverberation unit, instead of sampling a real space. Thus, it is possible to use a convolution reverb in place of a hardware machine. The techniques used to sample a reverberation unit are the same as the ones used to sample real spaces.
Electronic music[edit]
In electronic music convolution is the imposition of a spectral or rhythmic structure on a sound. Often this envelope or structure is taken from another sound. The convolution of two signals is the filtering of one through the other.[2] See applications of convolution.
Convolution Reverb Plugin
References[edit]
Convolution Reverb Algorithm
- ^http://www.libinst.com/mlsmeas.htm
- ^Zölzer, Udo, ed. (2002). DAFX:Digital Audio Effects, p.48–49. ISBN0471490784.
External links[edit]
- Acoustical Impulse Response Measurement with ALIKI: Includes comparison of methods for creating impulse responses
- Freeverb3 Impulse Response Processor: Opensource zero latency impulse response processor with source code; also includes a few different algorithmic reverberators
- Freeverb3 VST plugins A package of VST DSP effect plugins utilizing the Freeverb3 signal processing library.
- University of York, OpenAIR open source IR Library (over 70 sampled spaces; 2011-2017)