Unifying Probabilistic Models for Time-frequency Analysis

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2019-05-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
5
3352-3356
Series
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Abstract
In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts. They adapt to the incoming signal, quantify uncertainty, and measure correlation between the signal's amplitude and phase information, making time domain resynthesis straightforward. However, these models are still not widely used since they come at a high computational cost, and because they are formulated in such a way that it can be difficult to interpret all the modelling assumptions. By showing their equivalence to Spectral Mixture Gaussian processes, we illuminate the underlying model assumptions and provide a general framework for constructing more complex models that better approximate real-world signals. Our interpretation makes it intuitive to inspect, compare, and alter the models since all prior knowledge is encoded in the Gaussian process kernel functions. We utilise a state space representation to perform efficient inference via Kalman smoothing, and we demonstrate how our interpretation allows for efficient parameter learning in the frequency domain.
Description
Keywords
Gaussian processes, probabilistic time-frequency analysis, state space models
Other note
Citation
Wilkinson , W J , Riis Andersen , M , Reiss , J D , Stowell , D & Solin , A 2019 , Unifying Probabilistic Models for Time-frequency Analysis . in 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings . , 8682306 , IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings , IEEE , pp. 3352-3356 , IEEE International Conference on Acoustics, Speech, and Signal Processing , Brighton , United Kingdom , 12/05/2019 . https://doi.org/10.1109/ICASSP.2019.8682306