Assessment of background noise properties in time and time–frequency domains in the context of vibration-based local damage detection in real environment
Any measurement in condition monitoring applications is associated with disturbing noise.
Till now, most of the diagnostic procedures have assumed the Gaussian distribution for the …
Till now, most of the diagnostic procedures have assumed the Gaussian distribution for the …
PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components
To capture the slowly time-varying spectral content of real-world time-series, a common
paradigm is to partition the data into approximately stationary intervals and perform …
paradigm is to partition the data into approximately stationary intervals and perform …
Reduced-rank spectral mixtures Gaussian processes for probabilistic time–frequency representations
A Fradi, K Daoudi - Signal Processing, 2024 - Elsevier
Deterministic time–frequency representations are commonly used in signal processing,
particularly in audio processing. Whilst presenting many potential advantages, their …
particularly in audio processing. Whilst presenting many potential advantages, their …
End-to-end probabilistic inference for nonstationary audio analysis
A typical audio signal processing pipeline includes multiple disjoint analysis stages,
including calculation of a time-frequency representation followed by spectrogram-based …
including calculation of a time-frequency representation followed by spectrogram-based …
Sequential MCMC methods for audio signal enhancement
With the aim of addressing audio signal restoration as a sequential inference problem, we
build upon Gabor regression to propose a state-space model for audio time series …
build upon Gabor regression to propose a state-space model for audio time series …
Advances in software and spatio-temporal modelling with Gaussian processes
W Tebbutt - 2022 - repository.cam.ac.uk
This thesis concerns the use of Gaussian processes (GPs) as distributions over unknown
functions in Machine Learning and probabilistic modeling. GPs have been found to have …
functions in Machine Learning and probabilistic modeling. GPs have been found to have …
Variational Log-Power Spectral Tracking for Acoustic Signals
This paper proposes a generative hierarchical probabilistic model for acoustic signals where
both the frequency decomposition and log-power spectrum appear as latent variables. In …
both the frequency decomposition and log-power spectrum appear as latent variables. In …
Generative models for neural time series with structured domain priors
AH Song - 2022 - dspace.mit.edu
When I initially set out to research in the intersection of statistical signal processing and
neuroscience (neural signal processing), my research advisor, Professor Emery N. Brown …
neuroscience (neural signal processing), my research advisor, Professor Emery N. Brown …
[PDF][PDF] Essays in simulation and stochastic processes
D Mackinlay - 2021 - unsworks.unsw.edu.au
Essays in simulation and stochastic processes Page 1 Essays in simulation and stochastic
processes Author: Mackinlay, Daniel Publication Date: 2021 DOI: https://doi.org/10.26190/unsworks/22411 …
processes Author: Mackinlay, Daniel Publication Date: 2021 DOI: https://doi.org/10.26190/unsworks/22411 …
[PDF][PDF] An efficient Gaussian process framework for analysis of oscillations in nonstationary time series
Abstract We propose Piecewise Locally Stationary Oscillation (PLSO) state-space model for
decomposing nonstationary time series with slowly timevarying spectra into several …
decomposing nonstationary time series with slowly timevarying spectra into several …