This article describes software for granular synthesis of sound. The software features a graphical interface that enables easy creation and modification of sound clouds by deterministic fractal organization. Output sound clouds exist in multidimensional parameter–time space, and are constructed as a micropolyphony of statements of a single input melody or group of notes. The approach described here is an effective alternative to statistical methods, creating sounds with vitality and interest over a range of time scales. Standard techniques are used for the creation of individual grains. Innovation is demonstrated in the particular approach to fractal organization of the sound cloud and in the design of a smart interface to effect easy control of cloud morphology. The interface provides for intuitive control and reorganization of large amounts of data.

This article describes methods of sound synthesis based on auditory distortion products, often called combination tones. In 1856, Helmholtz was the first to identify sum and difference tones as products of auditory distortion. Today this phenomenon is well studied in the context of otoacoustic emissions, and the “distortion” is understood as a product of what is termed the cochlear amplifier. These tones have had a rich history in the music of improvisers and drone artists. Until now, the use of distortion tones in technological music has largely been rudimentary and dependent on very high amplitudes in order for the distortion products to be heard by audiences. Discussed here are synthesis methods to render these tones more easily audible and lend them the dynamic properties of traditional acoustic sound, thus making auditory distortion a practical domain for sound synthesis. An adaptation of single-sideband synthesis is particularly effective for capturing the dynamic properties of audio inputs in real time. Also presented is an analytic solution for matching up to four harmonics of a target spectrum. Most interestingly, the spatial imagery produced by these techniques is very distinctive, and over loudspeakers the normal assumptions of spatial hearing do not apply. Audio examples are provided that illustrate the discussion.

This article describes a synthesis technique based on the sonification of the dynamic behavior of a quantum particle enclosed in an infinite square well. More specifically, we sonify the momentum distribution of a one-dimensional Gaussian bouncing wave packet model. We have chosen this particular case because of its relative simplicity and interesting dynamic behavior, which makes it suitable for a novel sonification mapping that can be applied to standard synthesis techniques, resulting in the generation of appealing sounds. In addition, this sonification might provide useful insight into the behavior of the quantum particle. In particular, this model exhibits quantum revivals, minimizes uncertainty, and exhibits similarities to the case of a classical bouncing ball. The proposed model has been implemented in real time in both the Max/MSP and the Pure Data environments. The algorithm is based on concepts of additive synthesis where each oscillator describes the eigenfunctions that characterize the state evolution of the wave packet. We also provide an analysis of the sounds produced by the model from both a physical and a perceptual point of view.

In this article, we propose a control strategy for synthesized continuous-interaction sounds. The framework of our research is based on the action–object paradigm that describes the sound as the result of an action on an object and that presumes the existence of sound invariants (i.e., perceptually relevant signal morphologies that carry information about the action's or the object's attributes). Auditory cues are investigated here for the evocations of rubbing, scratching, and rolling interactions. A generic sound-synthesis model that simulates these interactions is detailed. We then suggest an intuitive control strategy that enables users to navigate continuously from one interaction to another in an “action space,” thereby offering the possibility to simulate morphed interactions—for instance, ones that morph between rubbing and rolling.

Histogram mapping synthesis (HMS) is a new technique for sound design based on cellular automata (CA). Cellular automata are computational models that create moving images. In the context of HMS, and based on a novel digital signal processing approach, these images are analyzed by histogram measurements, giving a sequence of histograms as a result. In a nutshell, these histogram sequences are converted into spectrograms that, in turn, are rendered into sounds. Unlike other CA-based systems, the HMS mapping process is not intuition-based, nor is it totally arbitrary; it is based instead on resemblances discovered between the components of the histogram sequences and the spectral components of the sounds. Our main concern is to address the problem of the sound-design limitations of synthesis techniques based on CA. These limitations stem, fundamentally, from the unpredictable and autonomous nature of these computational models. As a result, one of the main advantages of HMS is that it affords more control over the sound-design process than other sound-synthesis techniques using CA. The timbres that we have designed with HMS range from those that are novel to those that are imitations of sounds produced by acoustic means. All the sounds obtained present dynamic features, and many of them, including some of those that are novel, retain important characteristics of sounds produced by acoustic means.