Oral Session 3: Sound Synthesis

15:40-17:00 on Tuesday, 3rd September

P350 Lecture Theatre, Parkside

Chiar: Joshua Reiss
Exploring the Sound of Chaotic Oscillators via Parameter Spaces
Georg Essl

Chaotic oscillators are exciting sources for sound production due to their simplicity in implementation combined with their rich sonic output. However, the richness comes with difficulty of control, which is paramount to both their detailed understanding and in live musical performance. In this paper, we propose perceptually motivated parameter planes as a framework for studying the behavior of chaotic oscillators for musical use. Motivated by analysis via winding numbers, we extend traditional study of chaotic oscillators by using local features that are perceptually inspired. We illustrate the framework on the example of variations of the circle map. However, the framework is applicable for a wide range of sound synthesis algorithms with nontrivial parametric mappings.

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Large-scale Real-time Modular Physical Modeling Sound Synthesis
Stefan Bilbao, Michele Ducceschi and Craig Webb

Due to recent increases in computational power, physical modeling synthesis is now possible in real time even for relatively complex models. We present here a modular physical modeling instrument design, intended as a construction framework for string- and bar- based instruments, alongside a mechanical network allowing for arbitrary nonlinear interconnection. When multiple nonlinearities are present in a feedback setting, there are two major concerns. One is ensuring numerical stability, which can be approached using an energy-based framework. The other is coping with the computational cost associated with nonlinear solvers—standard iterative methods, such as Newton-Raphson, quickly become a computational bottleneck. Here, such iterative methods are sidestepped using an alternative energy conserving method, allowing for great reduction in computational expense or, alternatively, to real-time performance for very large-scale nonlinear physical modeling synthesis. Simulation and benchmarking results are presented.

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A perceptually inspired generative model of rigid-body contact sounds
James Traer, Maddie Cusimano and Josh McDermott

Contact between rigid-body objects produces a diversity of impact and friction sounds. These sounds can be synthesized with detailed simulations of the motion, vibration and sound radiation of the objects, but such synthesis is computationally expensive and prohibitively slow for many applications. Moreover, detailed physical simulations may not be necessary for perceptually compelling synthesis; humans infer ecologically relevant causes of sound, such as material categories, but not with arbitrary precision. We present a generative model of impact sounds which summarizes the effect of physical variables on acoustic features via statistical distributions fit to empirical measurements of object acoustics. Perceptual experiments show that sampling from these distributions allows efficient synthesis of realistic impact and scraping sounds that convey material, mass, and motion.

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Modelling Experts’ Decisions on Assigning Narrative Importances of Objects in a Radio Drama Mix
Emmanouil Theofanis Chourdakis, Lauren Ward, Matthew Paradis and Joshua D. Reiss

There is an increasing number of consumers of broadcast audio who suffer from a degree of hearing impairment. One of the methods developed for tackling this issue consists of creating customizable object-based audio mixes where users can attenuate parts of the mix using a simple complexity parameter. The method relies on the mixing engineer classifying audio objects in the mix according to their narrative importance. This paper focuses on automating this process. Individual tracks are classified based on their music, speech, or sound effect content. Then the decisions for assigning narrative importance to each segment of a radio drama mix are modelled using mixture distributions. Finally, the learned decisions and resultant mixes are evaluated using the Short Term Objective Intelligibility, with reference to the narrative importance selections made by the original producer. This approach has applications for providing customizable mixes for legacy content, or automatically generated media content where the engineer is not able to intervene.

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