Learning Gamelan

April 5, 2016 — August 5, 2022

convolution
functional analysis
music
neural nets
nonparametric
signal processing
sparser than thou

Attention conservation notice: Crib notes for a 2-year-long project which I ultimately abandoned in late 2018, about approximating convnet 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, and those will be actually interesting.

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. Mostly we handle musical signals via convnets which is not satisfying, and one feels one could do better with a more appropriate architecture. This project was about finding that architecture.

Some observations about theory are in recurrent/convolutional/state-space.

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