We propose a new biologically-inspired paradigm for universal audio coding based on neural spikes. Our approach is based on the generation of sparse 2-D representations of audio signals, dubbed as spikegrams. The spikegrams are generated by projecting the signal onto a set of overcomplete adaptive gammachirp (gammatone with additional tuning parameters) kernels. A masking model is applied to the spikegrams to remove inaudible spikes and to increase the coding efficiency. The paradigm is a first step towards the implementation of a high-quality audio encoder by further processing acoustical events generated in the spikegrams. Upon necessary optimization, our coding system operating at 1 bit/sample for sound sampled at 44.1kHz, is expected to deliver high quality audio for broadcasting, archiving, etc.
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