Spiking Music: Audio Compression with Event Based Auto-encoders
This addresses audio compression efficiency for digital systems, but it is incremental as it builds on existing auto-encoder methods with a novel event-based twist.
The paper tackled audio compression by using event-based encoding with a deep binary auto-encoder, achieving competitive compression/reconstruction trade-offs on the MAESTRO dataset of piano recordings, with unsupervised emergence of selectivity and synchrony between encoded events and piano key strikes.
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.