6,500 research outputs found
Statistical Mechanics and Visual Signal Processing
The nervous system solves a wide variety of problems in signal processing. In
many cases the performance of the nervous system is so good that it apporaches
fundamental physical limits, such as the limits imposed by diffraction and
photon shot noise in vision. In this paper we show how to use the language of
statistical field theory to address and solve problems in signal processing,
that is problems in which one must estimate some aspect of the environment from
the data in an array of sensors. In the field theory formulation the optimal
estimator can be written as an expectation value in an ensemble where the input
data act as external field. Problems at low signal-to-noise ratio can be solved
in perturbation theory, while high signal-to-noise ratios are treated with a
saddle-point approximation. These ideas are illustrated in detail by an example
of visual motion estimation which is chosen to model a problem solved by the
fly's brain. In this problem the optimal estimator has a rich structure,
adapting to various parameters of the environment such as the mean-square
contrast and the correlation time of contrast fluctuations. This structure is
in qualitative accord with existing measurements on motion sensitive neurons in
the fly's brain, and we argue that the adaptive properties of the optimal
estimator may help resolve conlficts among different interpretations of these
data. Finally we propose some crucial direct tests of the adaptive behavior.Comment: 34pp, LaTeX, PUPT-143
Bias in particle tracking acceleration measurement
We investigate sources of error in acceleration statistics from Lagrangian
Particle Tracking (LPT) data and demonstrate techniques to eliminate or
minimise bias errors introduced during processing. Numerical simulations of
particle tracking experiments in isotropic turbulence show that the main
sources of bias error arise from noise due to position uncertainty and
selection biases introduced during numerical differentiation. We outline the
use of independent measurements and filtering schemes to eliminate these
biases. Moreover, we test the validity of our approach in estimating the
statistical moments and probability densities of the Lagrangian acceleration.
Finally, we apply these techniques to experimental particle tracking data and
demonstrate their validity in practice with comparisons to available data from
literature. The general approach, which is not limited to acceleration
statistics, can be applied with as few as two cameras and permits a substantial
reduction in the spatial resolution and sampling rate required to adequately
measure statistics of Lagrangian acceleration
Sparseness-controlled adaptive algorithms for supervised and unsupervised system identification
In single-channel hands-free telephony, the acoustic coupling between the loudspeaker and
the microphone can be strong and this generates echoes that can degrade user experience.
Therefore, effective acoustic echo cancellation (AEC) is necessary to maintain a stable
system and hence improve the perceived voice quality of a call. Traditionally, adaptive
filters have been deployed in acoustic echo cancellers to estimate the acoustic impulse
responses (AIRs) using adaptive algorithms. The performances of a range of well-known
algorithms are studied in the context of both AEC and network echo cancellation (NEC).
It presents insights into their tracking performances under both time-invariant and time-varying
system conditions.
In the context of AEC, the level of sparseness in AIRs can vary greatly in a mobile
environment. When the response is strongly sparse, convergence of conventional
approaches is poor. Drawing on techniques originally developed for NEC, a class of time-domain
and a frequency-domain AEC algorithms are proposed that can not only work
well in both sparse and dispersive circumstances, but also adapt dynamically to the level
of sparseness using a new sparseness-controlled approach.
As it will be shown later that the early part of the acoustic echo path is sparse
while the late reverberant part of the acoustic path is dispersive, a novel approach to
an adaptive filter structure that consists of two time-domain partition blocks is proposed
such that different adaptive algorithms can be used for each part. By properly controlling
the mixing parameter for the partitioned blocks separately, where the block lengths are
controlled adaptively, the proposed partitioned block algorithm works well in both sparse
and dispersive time-varying circumstances.
A new insight into an analysis on the tracking performance of improved proportionate
NLMS (IPNLMS) is presented by deriving the expression for the mean-square error.
By employing the framework for both sparse and dispersive time-varying echo paths, this
work validates the analytic results in practical simulations for AEC.
The time-domain second-order statistic based blind SIMO identification algorithms,
which exploit the cross relation method, are investigated and then a technique with proportionate
step-size control for both sparse and dispersive system identification is also
developed
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
This paper is about alerting acoustic event detection and sound source
localisation in an urban scenario. Specifically, we are interested in spotting
the presence of horns, and sirens of emergency vehicles. In order to obtain a
reliable system able to operate robustly despite the presence of traffic noise,
which can be copious, unstructured and unpredictable, we propose to treat the
spectrograms of incoming stereo signals as images, and apply semantic
segmentation, based on a Unet architecture, to extract the target sound from
the background noise. In a multi-task learning scheme, together with signal
denoising, we perform acoustic event classification to identify the nature of
the alerting sound. Lastly, we use the denoised signals to localise the
acoustic source on the horizon plane, by regressing the direction of arrival of
the sound through a CNN architecture. Our experimental evaluation shows an
average classification rate of 94%, and a median absolute error on the
localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of
2.5{\deg} when operating on frames of 2.5s. The system offers excellent
performance in particularly challenging scenarios, where the noise level is
remarkably high.Comment: 6 pages, 9 figure
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