Ecologically-based Granular Synthesis

We present a granular synthesis (GS) technique that produces environmental-like sounds using sampled sound grains and meso-time control functions. This approach is related to physical modeling (PM) (Smith, 1992; VŠlimŠki & Takala, 1996) and traditional granular synthesis (GS) (Roads, 1996, 299; Truax, 1988) but we have worked on two issues that have been previously neglected in these techniques. The model (1) produces time patterns at ranges from ten milliseconds to several seconds (meso-level structure), and (2) uses as basic raw material short-duration sampled sound grains with complex spectral dynamics.

[1]  Barry Truax Chaotic Non-linear Systems and Digital Synthesis: An Exploratory Study , 1990, ICMC.

[2]  S. Handel,et al.  Chapter 12 – Timbre Perception and Auditory Object Identification , 1995 .

[3]  Barry Truax,et al.  Discovering Inner Complexity: Time Shifting and Transposition with a Real-Time Granulation Technique , 1994 .

[4]  Agostino Di Scipio Micro-time sonic design and timbre formation , 1994 .

[5]  Stephen McAdams,et al.  Recognition of sound sources and events , 1993 .

[6]  Conrado Silva,et al.  outline of a hybrid musical system , 2000 .

[7]  W H Warren,et al.  The Way the Ball Bounces: Visual and Auditory Perception of Elasticity and Control of the Bounce Pass , 1987, Perception.

[8]  Roger B. Dannenberg A Perspective on Computer Music , 1996 .

[9]  Claude Cadoz,et al.  The physical model: modeling and simulating the instrumental universe , 1991 .

[10]  Albert S. Bregman,et al.  The Auditory Scene. (Book Reviews: Auditory Scene Analysis. The Perceptual Organization of Sound.) , 1990 .

[11]  R. T. Schumacher,et al.  The transient behaviour of models of bowed-string motion. , 1995, Chaos.

[12]  P.P.J. van den Bosch,et al.  Modeling, identification, and simulation of dynamical systems , 1994 .

[13]  W H Warren,et al.  Auditory perception of breaking and bouncing events: a case study in ecological acoustics. , 1984, Journal of experimental psychology. Human perception and performance.

[14]  F. Richard Moore,et al.  Elements of computer music , 1990 .

[15]  X. Rodet Time — Domain Formant — Wave — Function Synthesis , 1984 .

[16]  Julius O. Smith,et al.  Physical Modeling Using Digital Waveguides , 1992 .

[17]  Curtis Roads,et al.  The Computer Music Tutorial , 1996 .

[18]  Max V. Mathews,et al.  Current directions in computer music research , 1989 .

[19]  R. Damper Introduction to Discrete-time Signals and Systems , 1995 .

[20]  Thomas F. Quatieri,et al.  Speech analysis/Synthesis based on a sinusoidal representation , 1986, IEEE Trans. Acoust. Speech Signal Process..

[21]  Barry Truax,et al.  Soundscape, Acoustic Communication and Environmental Sound Composition , 1996 .

[22]  Michael Clarke,et al.  Composing at the intersection of time and frequency , 1996, Organised Sound.

[23]  D. Gabor Acoustical Quanta and the Theory of Hearing , 1947, Nature.

[24]  Curtis Roads Musical Sound Transformation by Convolution , 1993, ICMC.

[25]  William W. Gaver Synthesizing auditory icons , 1993, INTERCHI.

[26]  Vesa Välimäki,et al.  Virtual musical instruments — natural sound using physical models , 1996, Organised Sound.

[27]  J. Ballas Common factors in the identification of an assortment of brief everyday sounds , 1993 .

[28]  Trevor Wishart,et al.  On Sonic Art , 1996 .

[29]  Giovanni De Poli,et al.  Pitch-synchronous granular synthesis , 1991 .

[30]  Barry Truax,et al.  Real-Time Granular Synthesis with a Digital Signal Processor , 1988 .