The synthesis of sound textures, such as rain, wind, or crowds, is an important application for cinema, multimedia creation, games and installations. However, despite the clearly defined requirments of naturalness and flexibility, no automatic method has yet found widespread use. After clarifying the definition, terminology, and usages of sound texture synthesis, we will give an overview of the many existing methods and approaches, and the few available software implementations, and classify them by the synthesis model they are based on, such as subtractive or additive synthesis, granular synthesis, corpus-based concatenative synthesis, wavelets, or physical modeling. Additionally, an overview is given over analysis methods used for sound texture synthesis, such as segmentation, statistical modeling, timbral analysis, and modeling of transitions. 2

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IRCAM has a long experience in analysis, synthesis and transformation of voice. Natural voice transformations are of great interest for many applications and can be combine with text-to-speech system, leading to a powerful creation tool. We present research conducted at IRCAM on voice transformations for the last few years. Transformations can be achieved in a global way by modifying pitch, spectral envelope, durations etc. While it sacrifices the possibility to attain a specific target voice, the approach allows the production of new voices of a high degree of naturalness with different gender and age, modified vocal quality, or another speech style. These transformations can be applied in realtime using ircamTools TR A X.Transformation can also be done in a more specific way in order to transform a voice towards the voice of a target speaker. Finally, we present some recent research on the transformation of expressivity.

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Interactive navigation within geometric, feature-based database representations allows expressive musical performances and installations. Once mapped to the feature space, the user’s position in a physical interaction setup (e.g. a multitouch tablet) can be used to select elements or trigger audio events. Hence physical displacements are directly connected to the evolution of sonic characteristics — a property we call analytic sound–control correspondence. However, automatically computed representations have a complex geometry which is unlikely to fit the interaction setup optimally. After a review of related work, we present a physical model-based algorithm that redistributes the representation within a user-defined region according to a user-defined density. The algorithm is designed to preserve the analytic sound-control correspondence property as much as possible, and uses a physical analogy between the triangulated database representation and a truss structure. After preliminary pre-uniformisation steps, internal repulsive forces help to spread points across the whole region until a target density is reached. We measure the algorithm performance relative to its ability to produce representations corresponding to user-specified features and to preserve analytic sound–control correspondence during a standard density-uniformisation task. Quantitative measures and visual evaluation outline the excellent performances of the algorithm, as well as the interest of the pre-uniformisation steps.

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