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Multiresolution sampling procedure for analysis and synthesis of texture images
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NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Fast texture synthesis using tree-structured vector quantization
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Time Series Analysis: Forecasting and Control
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Texture Mixing and Texture Movie Synthesis Using Statistical Learning
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Information Theory
Sound-by-numbers: motion-driven sound synthesis
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Real-time rendering of aerodynamic sound using sound textures based on computational fluid dynamics
ACM SIGGRAPH 2003 Papers
Network intrusion visualization with NIVA, an intrusion detection visual and haptic analyzer
Information Visualization
TAPESTREA: sound scene modeling by example
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Adaptive Concatenative Sound Synthesis and Its Application to Micromontage Composition
Computer Music Journal
TAPESTREA: a new way to design sound
MM '09 Proceedings of the 17th ACM international conference on Multimedia
TAPESTREA: a new way to design sound
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Re-texturing the sonic environment
Proceedings of the 5th Audio Mostly Conference: A Conference on Interaction with Sound
ACM SIGGRAPH 2011 papers
Motion-driven concatenative synthesis of cloth sounds
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
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Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
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In this paper we present a statistical learning algorithm for synthesizing new random instances of a sound texture given an example of such a texture as input.A large class of natural and artificial sounds such as rain, waterfall, traffic noises, people babble, machine noises, etc., can be regarded as sound textures--sound signals that are approximately stationary at some scale. Treating the input sound texture as a sample of a stochastic process, we construct a tree representing a hierarchical wavelet transform of the signal. From this tree, new random trees are generated by learning and sampling the conditional probabilities of the paths in the original tree. Transformation of these random trees back into signals results in new sound textures that closely resemble the sonic impression of the original sound source but without exactly repeating it. Applications of this method are abundant and include, for example, automatic generation of sound effects, creative musical and sonic manipulations, and virtual reality sonification. Examples are visually demonstrated in the paper and acoustically demonstrated in an accompanying web site.