Artificial Reverberation

There are a variety of approaches to synthesizing the effect of a reverberant space. Those based on direct measurement of a particular room response (convolution techniques) tend to be less extensible but gaining in popularity. The use of three-dimensional wave-based modeling techniques is limited by computational requirements. Most work in artifically simulating reverberation has been on “physically- and perceptually-informed” techniques that seek to create parametrically-controllable systems. These models can produce very good reverberant responses though they generally cannot be made to correlate with actual room measurements.

Two excellent overviews of artificial reverbation developments are given by

• Gardner, W. G. “Reverberation Algorithms,” in Applications of Signal Processing to Audio and Acoustics, M. Kahrs and K. Brandenburg, Eds., Kluwer Academic, Norwell, MA, 1997.
• Välimäki, V., Parker, J., Savioja, L. Smith, J. O., Abel, J. “Fifty Years of Artificial Reverberation, IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20, No. 5, July 2012.

Artificial reverberation methods can generally be grouped into the following categories:

1. convolutional: an input signal is convolved with a recorded or estimated impulse response of an acoustic space;
2. computational acoustics: an input signal is fed into a system that simulates acoustic propagation in a modeled geometry;
3. delay networks: an input signal is delayed, filtered and fed back along a number of simulated propagation paths to achieve a parameterized reverberation characteristic.

This section focuses primarily on the use of delay networks for articial reverberation.