Learning Gamelan

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 thence 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.


  • HazyResearch/state-spaces: Sequence Modeling with Structured State Spaces (A. Gu et al. 2021) did what I was trying to do in learning gamelan, but better.

    Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency. We introduce a simple sequence model inspired by control systems that generalizes these approaches while addressing their shortcomings. The Linear State-Space Layer (LSSL) maps a sequence u↦y by simply simulating a linear continuous-time state-space representation xΛ™=Ax+Bu,y=Cx+Du. Theoretically, we show that LSSL models are closely related to the three aforementioned families of models and inherit their strengths. For example, they generalize convolutions to continuous-time, explain common RNN heuristics, and share features of NDEs such as time-scale adaptation. We then incorporate and generalize recent theory on continuous-time memorization to introduce a trainable subset of structured matrices A that endow LSSLs with long-range memory. Empirically, stacking LSSL layers into a simple deep neural network obtains state-of-the-art results across time series benchmarks for long dependencies in sequential image classification, real-world healthcare regression tasks, and speech. On a difficult speech classification task with length-16000 sequences, LSSL outperforms prior approaches by 24 accuracy points, and even outperforms baselines that use hand-crafted features on 100x shorter sequences.


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