1,185 research outputs found
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain
Gaussian process (GP) audio source separation is a time-domain approach that
circumvents the inherent phase approximation issue of spectrogram based
methods. Furthermore, through its kernel, GPs elegantly incorporate prior
knowledge about the sources into the separation model. Despite these compelling
advantages, the computational complexity of GP inference scales cubically with
the number of audio samples. As a result, source separation GP models have been
restricted to the analysis of short audio frames. We introduce an efficient
application of GPs to time-domain audio source separation, without compromising
performance. For this purpose, we used GP regression, together with spectral
mixture kernels, and variational sparse GPs. We compared our method with
LD-PSDTF (positive semi-definite tensor factorization), KL-NMF
(Kullback-Leibler non-negative matrix factorization), and IS-NMF (Itakura-Saito
NMF). Results show that the proposed method outperforms these techniques.Comment: Paper submitted to the 44th International Conference on Acoustics,
Speech, and Signal Processing, ICASSP 2019. To be held in Brighton, United
Kingdom, between May 12 and May 17, 201
Sparse Linear Models applied to Power Quality Disturbance Classification
Power quality (PQ) analysis describes the non-pure electric signals that are
usually present in electric power systems. The automatic recognition of PQ
disturbances can be seen as a pattern recognition problem, in which different
types of waveform distortion are differentiated based on their features.
Similar to other quasi-stationary signals, PQ disturbances can be decomposed
into time-frequency dependent components by using time-frequency or time-scale
transforms, also known as dictionaries. These dictionaries are used in the
feature extraction step in pattern recognition systems. Short-time Fourier,
Wavelets and Stockwell transforms are some of the most common dictionaries used
in the PQ community, aiming to achieve a better signal representation. To the
best of our knowledge, previous works about PQ disturbance classification have
been restricted to the use of one among several available dictionaries. Taking
advantage of the theory behind sparse linear models (SLM), we introduce a
sparse method for PQ representation, starting from overcomplete dictionaries.
In particular, we apply Group Lasso. We employ different types of
time-frequency (or time-scale) dictionaries to characterize the PQ
disturbances, and evaluate their performance under different pattern
recognition algorithms. We show that the SLM reduce the PQ classification
complexity promoting sparse basis selection, and improving the classification
accuracy
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes
A latent force model is a Gaussian process with a covariance function
inspired by a differential operator. Such covariance function is obtained by
performing convolution integrals between Green's functions associated to the
differential operators, and covariance functions associated to latent
functions. In the classical formulation of latent force models, the covariance
functions are obtained analytically by solving a double integral, leading to
expressions that involve numerical solutions of different types of error
functions. In consequence, the covariance matrix calculation is considerably
expensive, because it requires the evaluation of one or more of these error
functions. In this paper, we use random Fourier features to approximate the
solution of these double integrals obtaining simpler analytical expressions for
such covariance functions. We show experimental results using ordinary
differential operators and provide an extension to build general kernel
functions for convolved multiple output Gaussian processes.Comment: 10 pages, 4 figures, accepted by UAI 201
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