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Musical Attractors: A New Method for Audio Synthesis
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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