Artificial reverberation algorithms are used in every commercial studio to add life to dry recordings (recordings that don't have any reverberation).  Real reverberation consists of a large number of discrete echoes that would need a large amount of processing power to exactly recreate on a computer.  Most artificial reverberation algorithms attempt to model real room reverberation by reproducing only the salient characteristics of those rooms.  For example, they can use a model to simulate the individual early echoes of a room, and another model to simulate the late reverberation, perceived as being an exponentially decaying white noise.

This thesis will focus on the late reverberation synthesis.  In order to create a density of echoes that approximate the decay of a real reverberation, most artificial algorithms use feedback loops and delay elements.  The output of these algorithms (the reverberated signal) is produced by repeating the input signal (the signal to be reverberated) thousands of times per second to produce a density of echoes that is so high that it sounds like white noise.  The frequency response of most of these reverberation algorithms contains discrete frequency peaks.  When the echoes produced by the delay lines occur at a fixed rate, certain frequencies resonate more than others during the reverberation decay, which does not sound natural.  One solution to help minimize this effect is to use more feedback loops.  However, this solution requires more memory and computation.

This thesis proposes another alternative to produce a better reverberation.  By changing the rate of the echoes in real time, we should be able to vary the location of the peaks in the reverberation frequency response in time.  This avoids the build-up of resonances at specific frequency locations.  It should also make the resulting reverberation sound much smoother.  This solution would be especially attractive to applications that have limited memory access and computation power, such as game platforms or multimedia application.  Game platforms typically have 1 or 2 MBytes of Random Access Memory (RAM) for both audio samples and effects such as reverb.  Since the gaming market pushes for better sound quality, greater sound diversity, and more special effects, the memory needs are increased and any memory saving is highly desirable.

Chapter 2 will review the properties of room acoustics and introduce basic artificial reverberation algorithms, such as all-pass and comb filters.  It will then summarize different approaches that have been used to create artificial reverberation.  Chapter 3 will then focus on a specific reverberation algorithm called Feedback Delay Network (FDN) that is used in most of the recent artificial reverberation literature.  It will describe how basic comb and all-pass filters can be assembled to form this general network.  Chapter 4 will show how modulation can be used in an FDN to enhance its sound, and will review several modulation and interpolation types.  It will also review previous attempts to use modulation in reverberation algorithms.  Chapter 5 will describe the design of our modulated FDN algorithm, including the choice of modulation and interpolation methods.  It will then detail the performance and memory consumption of the design, and present the result of a listening test comparing it to a non-modulated algorithm containing more delay lines.