17,551 research outputs found
Perceptually Motivated Wavelet Packet Transform for Bioacoustic Signal Enhancement
A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and Ephraim–Malah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions
Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask
Singing voice separation based on deep learning relies on the usage of
time-frequency masking. In many cases the masking process is not a learnable
function or is not encapsulated into the deep learning optimization.
Consequently, most of the existing methods rely on a post processing step using
the generalized Wiener filtering. This work proposes a method that learns and
optimizes (during training) a source-dependent mask and does not need the
aforementioned post processing step. We introduce a recurrent inference
algorithm, a sparse transformation step to improve the mask generation process,
and a learned denoising filter. Obtained results show an increase of 0.49 dB
for the signal to distortion ratio and 0.30 dB for the signal to interference
ratio, compared to previous state-of-the-art approaches for monaural singing
voice separation
Ringing effects reduction by improved deconvolution algorithm Application to A370 CFHT image of gravitational arcs
We develop a self-consistent automatic procedure to restore informations from
astronomical observations. It relies on both a new deconvolution algorithm
called LBCA (Lower Bound Constraint Algorithm) and the use of the Wiener
filter. In order to explore its scientific potential for strong and weak
gravitational lensing, we process a CFHT image of the galaxies cluster Abell
370 which exhibits spectacular strong gravitational lensing effects. A high
quality restoration is here of particular interest to map the dark matter
within the cluster. We show that the LBCA turns out specially efficient to
reduce ringing effects introduced by classical deconvolution algorithms in
images with a high background. The method allows us to make a blind detection
of the radial arc and to recover morphological properties similar to
thoseobserved from HST data. We also show that the Wiener filter is suitable to
stop the iterative process before noise amplification, using only the
unrestored data.Comment: A&A in press 9 pages 9 figure
Joint Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications
This paper introduces a novel feedback-control based particle filter for the
solution of the filtering problem with data association uncertainty. The
particle filter is referred to as the joint probabilistic data
association-feedback particle filter (JPDA-FPF). The JPDA-FPF is based on the
feedback particle filter introduced in our earlier papers. The remarkable
conclusion of our paper is that the JPDA-FPF algorithm retains the innovation
error-based feedback structure of the feedback particle filter, even with data
association uncertainty in the general nonlinear case. The theoretical results
are illustrated with the aid of two numerical example problems drawn from
multiple target tracking applications.Comment: In Proc. of the 2012 American Control Conferenc
A Phase-space Formulation of the Belavkin-Kushner-Stratonovich Filtering Equation for Nonlinear Quantum Stochastic Systems
This paper is concerned with a filtering problem for a class of nonlinear
quantum stochastic systems with multichannel nondemolition measurements. The
system-observation dynamics are governed by a Markovian Hudson-Parthasarathy
quantum stochastic differential equation driven by quantum Wiener processes of
bosonic fields in vacuum state. The Hamiltonian and system-field coupling
operators, as functions of the system variables, are represented in a Weyl
quantization form. Using the Wigner-Moyal phase-space framework, we obtain a
stochastic integro-differential equation for the posterior quasi-characteristic
function (QCF) of the system conditioned on the measurements. This equation is
a spatial Fourier domain representation of the Belavkin-Kushner-Stratonovich
stochastic master equation driven by the innovation process associated with the
measurements. We also discuss a more specific form of the posterior QCF
dynamics in the case of linear system-field coupling and outline a Gaussian
approximation of the posterior quantum state.Comment: 12 pages, a brief version of this paper to be submitted to the IEEE
2016 Conference on Norbert Wiener in the 21st Century, 13-15 July, Melbourne,
Australi
A Quantum Langevin Formulation of Risk-Sensitive Optimal Control
In this paper we formulate a risk-sensitive optimal control problem for
continuously monitored open quantum systems modelled by quantum Langevin
equations. The optimal controller is expressed in terms of a modified
conditional state, which we call a risk-sensitive state, that represents
measurement knowledge tempered by the control purpose. One of the two
components of the optimal controller is dynamic, a filter that computes the
risk-sensitive state.
The second component is an optimal control feedback function that is found by
solving the dynamic programming equation. The optimal controller can be
implemented using classical electronics.
The ideas are illustrated using an example of feedback control of a two-level
atom
Efficient Wiener filtering without preconditioning
We present a new approach to calculate the Wiener filter solution of general
data sets. It is trivial to implement, flexible, numerically absolutely stable,
and guaranteed to converge. Most importantly, it does not require an ingenious
choice of preconditioner to work well. The method is capable of taking into
account inhomogeneous noise distributions and arbitrary mask geometries. It
iteratively builds up the signal reconstruction by means of a messenger field,
introduced to mediate between the different preferred bases in which signal and
noise properties can be specified most conveniently. Using cosmic microwave
background (CMB) radiation data as a showcase, we demonstrate the capabilities
of our scheme by computing Wiener filtered WMAP7 temperature and polarization
maps at full resolution for the first time. We show how the algorithm can be
modified to synthesize fluctuation maps, which, combined with the Wiener filter
solution, result in unbiased constrained signal realizations, consistent with
the observations. The algorithm performs well even on simulated CMB maps with
Planck resolution and dynamic range.Comment: 5 pages, 2 figures. Submitted to Astronomy and Astrophysics. Replaced
to match published versio
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