  Neurons in the brain communicate information via punctual events called
spikes. The timing of spikes is thought to carry rich information, but it is
not clear how to leverage this in digital systems. We demonstrate that
event-based encoding is efficient for audio compression. To build this
event-based representation we use a deep binary auto-encoder, and under high
sparsity pressure, the model enters a regime where the binary event matrix is
stored more efficiently with sparse matrix storage algorithms. We test this on
the large MAESTRO dataset of piano recordings against vector quantized
auto-encoders. Not only does our "Spiking Music compression" algorithm achieve
a competitive compression/reconstruction trade-off, but selectivity and
synchrony between encoded events and piano key strikes emerge without
supervision in the sparse regime.
