Learning Gamelan
April 5, 2016 — August 5, 2022
Crib notes for a 2-year-long project which I ultimately abandoned in late 2018, about approximating convnets with recurrent neural networks for analysing time series. This project currently exists purely as LaTeX files on my hard drive, which need to be imported for future reference. I did learn some useful tricks along the way about controlling the poles of IIR filters for learning by gradient descent. Most of the ideas were about how to learn a sparse, linear, time-invariant filterbank have been surpassed by S4 models
I feel a certain class of audio signal should be easy to decompose and then learn in a musically useful way; ones approximated by LTI, nearly-linear, nearly-additive filterbanks with sparse activations. At the time we mostly we handled musical signals via convnets, which is not satisfying, and I felt we could do better with a more appropriate architecture. This project was about finding that architecture.
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