6,207 research outputs found
Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems
Photolethysmographic imaging (PPGI) is a widefield non-contact biophotonic
technology able to remotely monitor cardiovascular function over anatomical
areas. Though spatial context can provide increased physiological insight,
existing PPGI systems rely on coarse spatial averaging with no anatomical
priors for assessing arterial pulsatility. Here, we developed a continuous
probabilistic pulsatility model for importance-weighted blood pulse waveform
extraction. Using a data-driven approach, the model was constructed using a 23
participant sample with large demographic variation (11/12 female/male, age
11-60 years, BMI 16.4-35.1 kgm). Using time-synchronized
ground-truth waveforms, spatial correlation priors were computed and projected
into a co-aligned importance-weighted Cartesian space. A modified
Parzen-Rosenblatt kernel density estimation method was used to compute the
continuous resolution-agnostic probabilistic pulsatility model. The model
identified locations that consistently exhibited pulsatility across the sample.
Blood pulse waveform signals extracted with the model exhibited significantly
stronger temporal correlation () and spectral SNR ()
compared to uniform spatial averaging. Heart rate estimation was in strong
agreement with true heart rate (, error
bpm)
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization
Reducing the interference noise in a monaural noisy speech signal has been a
challenging task for many years. Compared to traditional unsupervised speech
enhancement methods, e.g., Wiener filtering, supervised approaches, such as
algorithms based on hidden Markov models (HMM), lead to higher-quality enhanced
speech signals. However, the main practical difficulty of these approaches is
that for each noise type a model is required to be trained a priori. In this
paper, we investigate a new class of supervised speech denoising algorithms
using nonnegative matrix factorization (NMF). We propose a novel speech
enhancement method that is based on a Bayesian formulation of NMF (BNMF). To
circumvent the mismatch problem between the training and testing stages, we
propose two solutions. First, we use an HMM in combination with BNMF (BNMF-HMM)
to derive a minimum mean square error (MMSE) estimator for the speech signal
with no information about the underlying noise type. Second, we suggest a
scheme to learn the required noise BNMF model online, which is then used to
develop an unsupervised speech enhancement system. Extensive experiments are
carried out to investigate the performance of the proposed methods under
different conditions. Moreover, we compare the performance of the developed
algorithms with state-of-the-art speech enhancement schemes using various
objective measures. Our simulations show that the proposed BNMF-based methods
outperform the competing algorithms substantially
ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Symbol detection plays an important role in the implementation of digital
receivers. In this work, we propose ViterbiNet, which is a data-driven symbol
detector that does not require channel state information (CSI). ViterbiNet is
obtained by integrating deep neural networks (DNNs) into the Viterbi algorithm.
We identify the specific parts of the Viterbi algorithm that are
channel-model-based, and design a DNN to implement only those computations,
leaving the rest of the algorithm structure intact. We then propose a
meta-learning based approach to train ViterbiNet online based on recent
decisions, allowing the receiver to track dynamic channel conditions without
requiring new training samples for every coherence block. Our numerical
evaluations demonstrate that the performance of ViterbiNet, which is ignorant
of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable
of tracking time-varying channels without needing instantaneous CSI or
additional training data. Moreover, unlike conventional Viterbi detection,
ViterbiNet is robust to CSI uncertainty, and it can be reliably implemented in
complex channel models with constrained computational burden. More broadly, our
results demonstrate the conceptual benefit of designing communication systems
to that integrate DNNs into established algorithms.Comment: arXiv admin note: text overlap with arXiv:2002.0780
Non-Local Compressive Sensing Based SAR Tomography
Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse
reconstruction problem and, hence, can be solved using compressive sensing (CS)
algorithms. This paper proposes solutions for two notorious problems in this
field: 1) TomoSAR requires a high number of data sets, which makes the
technique expensive. However, it can be shown that the number of acquisitions
and the signal-to-noise ratio (SNR) can be traded off against each other,
because it is asymptotically only the product of the number of acquisitions and
SNR that determines the reconstruction quality. We propose to increase SNR by
integrating non-local estimation into the inversion and show that a reasonable
reconstruction of buildings from only seven interferograms is feasible. 2)
CS-based inversion is computationally expensive and therefore barely suitable
for large-scale applications. We introduce a new fast and accurate algorithm
for solving the non-local L1-L2-minimization problem, central to CS-based
reconstruction algorithms. The applicability of the algorithm is demonstrated
using simulated data and TerraSAR-X high-resolution spotlight images over an
area in Munich, Germany.Comment: 10 page
Evolutionary-based sparse regression for the experimental identification of duffing oscillator
In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation data. Our purpose is to identify the Coulomb friction terms as part of the ordinary differential equation of the system. Correct identification of this nonlinear system using sparse identification is hugely dependent on selecting the correct form of nonlinearity included in the function library. Consequently, in this work, the evolutionary-based sparse identification is replacing the need for user knowledge when constructing the library in sparse identification. Constructing the library based on the data-driven evolutionary approach is an effective way to extend the space of nonlinear functions, allowing for the sparse regression to be applied on an extensive space of functions. The results show that the method provides an effective algorithm for the purpose of unveiling the physical nature of the Duffing oscillator. In addition, the robustness of the identification algorithm is investigated for various levels of noise in simulation. The proposed method has possible applications to other nonlinear dynamic systems in mechatronics, robotics, and electronics
Non-Smooth Variational Data Assimilation with Sparse Priors
This paper proposes an extension to the classical 3D variational data
assimilation approach by explicitly incorporating as a prior information, the
transform-domain sparsity observed in a large class of geophysical signals. In
particular, the proposed framework extends the maximum likelihood estimation of
the analysis state to the maximum a posteriori estimator, from a Bayesian
perspective. The promise of the methodology is demonstrated via application to
a 1D synthetic example
Single Channel Speech Enhancement Using Outlier Detection
Distortion of the underlying speech is a common problem for single-channel
speech enhancement algorithms, and hinders such methods from being used more
extensively. A dictionary based speech enhancement method that emphasizes
preserving the underlying speech is proposed. Spectral patches of clean speech
are sampled and clustered to train a dictionary. Given a noisy speech spectral
patch, the best matching dictionary entry is selected and used to estimate the
noise power at each time-frequency bin. The noise estimation step is formulated
as an outlier detection problem, where the noise at each bin is assumed present
only if it is an outlier to the corresponding bin of the best matching
dictionary entry. This framework assigns higher priority in removing spectral
elements that strongly deviate from a typical spoken unit stored in the trained
dictionary. Even without the aid of a separate noise model, this method can
achieve significant noise reduction for various non-stationary noises, while
effectively preserving the underlying speech in more challenging noisy
environments
A Perceptual Weighting Filter Loss for DNN Training in Speech Enhancement
Single-channel speech enhancement with deep neural networks (DNNs) has shown
promising performance and is thus intensively being studied. In this paper,
instead of applying the mean squared error (MSE) as the loss function during
DNN training for speech enhancement, we design a perceptual weighting filter
loss motivated by the weighting filter as it is employed in
analysis-by-synthesis speech coding, e.g., in code-excited linear prediction
(CELP). The experimental results show that the proposed simple loss function
improves the speech enhancement performance compared to a reference DNN with
MSE loss in terms of perceptual quality and noise attenuation. The proposed
loss function can be advantageously applied to an existing DNN-based speech
enhancement system, without modification of the DNN topology for speech
enhancement. The source code for the proposed approach is made available
Enhanced Robot Audition Based on Microphone Array Source Separation with Post-Filter
We propose a system that gives a mobile robot the ability to separate
simultaneous sound sources. A microphone array is used along with a real-time
dedicated implementation of Geometric Source Separation and a post-filter that
gives us a further reduction of interferences from other sources. We present
results and comparisons for separation of multiple non-stationary speech
sources combined with noise sources. The main advantage of our approach for
mobile robots resides in the fact that both the frequency-domain Geometric
Source Separation algorithm and the post-filter are able to adapt rapidly to
new sources and non-stationarity. Separation results are presented for three
simultaneous interfering speakers in the presence of noise. A reduction of log
spectral distortion (LSD) and increase of signal-to-noise ratio (SNR) of
approximately 10 dB and 14 dB are observed.Comment: 6 page
Source Number Estimation via Entropy Estimation of Eigenvalues (EEE) in Gaussian and Non-Gaussian Noise
In this paper, a novel method based on the entropy estimation of the
observation space eigenvalues is proposed to estimate the number of the sources
in Gaussian and Non-Gaussian noise. In this method, the eigenvalues of
correlation matrix of the observation space will be divided by two sets:
eigenvalues of signal subspace and eigenvalues of noise subspace. We will use
estimated entropy of eigenvalues to determine the number of sources. In this
method we do not need any a priory information about signals and noise. The
advantages of the proposed algorithm based on the performance is compared with
the existing methods in the presence of Gaussian and Non-Gaussian noise. We
have shown that our proposed method outperforms those methods in the
literature, for different values of observation time and Signal to Noise Ratio,
i. e. SNR. It is shown that the algorithm is consistent and also its
probability of false alarm and probability of missed detection tend to zero for
long observation time.Comment: 24 Pages, 9 Figures, This paper is submitted to IEEE Transactions on
Signal Processin
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