Learning spectrograms with convolutional spectral kernels

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Volume Title
Conference article in proceedings
Date
2020
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Language
en
Pages
3826-3836
Series
The 23rd International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, Volume 108
Abstract
We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolution between two imaginary radial basis functions. We present a principled framework to interpret CSK, as well as other deep probabilistic models, using approximated Fourier transform, yielding a concise representation of input-frequency spectrogram. Observing through the lens of the spectrogram, we provide insight on the interpretability of deep models. We then infer the functional hyperparameters using scalable variational and MCMC methods. On small- and medium-sized spatiotemporal datasets, we demonstrate improved generalization of GP models when equipped with CSK, and their capability to extract non-stationary periodic patterns.
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Citation
Shen , Z , Heinonen , M & Kaski , S 2020 , Learning spectrograms with convolutional spectral kernels . in S Chiappa & R Calandra (eds) , The 23rd International Conference on Artificial Intelligence and Statistics . Proceedings of Machine Learning Research , vol. 108 , JMLR , pp. 3826-3836 , International Conference on Artificial Intelligence and Statistics , Palermo , Italy , 03/06/2020 . < http://proceedings.mlr.press/v108/shen20a.html >