11,925 research outputs found
Denoising Deep Neural Networks Based Voice Activity Detection
Recently, the deep-belief-networks (DBN) based voice activity detection (VAD)
has been proposed. It is powerful in fusing the advantages of multiple
features, and achieves the state-of-the-art performance. However, the deep
layers of the DBN-based VAD do not show an apparent superiority to the
shallower layers. In this paper, we propose a denoising-deep-neural-network
(DDNN) based VAD to address the aforementioned problem. Specifically, we
pre-train a deep neural network in a special unsupervised denoising greedy
layer-wise mode, and then fine-tune the whole network in a supervised way by
the common back-propagation algorithm. In the pre-training phase, we take the
noisy speech signals as the visible layer and try to extract a new feature that
minimizes the reconstruction cross-entropy loss between the noisy speech
signals and its corresponding clean speech signals. Experimental results show
that the proposed DDNN-based VAD not only outperforms the DBN-based VAD but
also shows an apparent performance improvement of the deep layers over
shallower layers.Comment: This paper has been accepted by IEEE ICASSP-2013, and will be
published online after May, 201
Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification
Measurement of blood oxygen saturation (sO2) by optical imaging oximetry provides invaluable insight into local tissue functions and metabolism. Despite different embodiments and modalities, all label-free optical-imaging oximetry techniques utilize the same principle of sO2-dependent spectral contrast from haemoglobin. Traditional approaches for quantifying sO2 often rely on analytical models that are fitted by the spectral measurements. These approaches in practice suffer from uncertainties due to biological variability, tissue geometry, light scattering, systemic spectral bias, and variations in the experimental conditions. Here, we propose a new data-driven approach, termed deep spectral learning (DSL), to achieve oximetry that is highly robust to experimental variations and, more importantly, able to provide uncertainty quantification for each sO2 prediction. To demonstrate the robustness and generalizability of DSL, we analyse data from two visible light optical coherence tomography (vis-OCT) setups across two separate in vivo experiments on rat retinas. Predictions made by DSL are highly adaptive to experimental variabilities as well as the depth-dependent backscattering spectra. Two neural-network-based models are tested and compared with the traditional least-squares fitting (LSF) method. The DSL-predicted sO2 shows significantly lower mean-square errors than those of the LSF. For the first time, we have demonstrated en face maps of retinal oximetry along with a pixel-wise confidence assessment. Our DSL overcomes several limitations of traditional approaches and provides a more flexible, robust, and reliable deep learning approach for in vivo non-invasive label-free optical oximetry.R01 CA224911 - NCI NIH HHS; R01 CA232015 - NCI NIH HHS; R01 NS108464 - NINDS NIH HHS; R21 EY029412 - NEI NIH HHSAccepted manuscrip
First born model for reflection-mode Fourier ptychographic microscopy
We validate a first Born approximation based model for Reflection-mode Fourier ptychography under the semi-infinite boundary condition. Our model enables optical thickness and absorption recovery with enhanced resolution from thin samples.Published versio
Probing anisotropic superfluidity of rashbons in atomic Fermi gases
Motivated by the prospect of realizing a Fermi gas of K atoms with a
synthetic non-Abelian gauge field, we investigate theoretically a strongly
interacting Fermi gas in the presence of a Rashba spin-orbit coupling. As the
two-fold spin degeneracy is lifted by spin-orbit interaction, bound pairs with
mixed singlet and triplet pairings (referred to as rashbons) emerge, leading to
an anisotropic superfluid. We show that this anisotropic superfluidity can be
probed via measuring the momentum distribution and single-particle spectral
function in a trapped atomic K cloud near a Feshbach resonance.Comment: 4 pages, 5 figure
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