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Musical Attractors: A New Method for Audio Synthesis

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In this paper, we use mathematical tools developed for chaos theory and time series analysis and apply them to the analysis and resynthesis of musical instruments. In particular, we can embed a basic one-dimensional audio signal time series within a higher-dimensional space to uncover the underlying generative attractor. Röbel (1999, 2001) described a neural-net model for audio sound synthesis based on attractor reconstruction. We present a different methodology inspired by Kaplan and Glass (1995) to resynthesize the signal based on time-lag embedding in different numbers of dimensions, and suggest techniques for choosing the approximate embedding dimension to optimize the quality of the synthesized audio.

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