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Differential Graph-Based Coding of Spikes in a Biologically-Inspired Universal Audio Coder
In a previous work we showed that it is possible to code audio materials using a biologically-inspired universal audio coder based on matching pursuit. The best atoms/kernels chosen by matching pursuit are represented by spikes to reflect the biologically-inspired nature of the algorithm. In that work, each spike or atom was defined by parameters such as timing, channel frequency, amplitude, chirp factor, etc. that were encoded independently. However, encoding each atom/spike as a separate entity is very bit consuming. In the present work, we propose algorithms to encode only the difference between parameters associated with spikes. Hence, we assume that each spike/atom is a node in a graph and choose the sequence of spikes that will minimize the differential encoding costs. Methods based on minimum spanning tree and travelling salesman are proposed and compared for the graph-based optimization of the code.
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