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Microphone array Beampattern Characterization for Hands-free Speech Applications

Microphone Array Beampattern Characterization for Hands-free...

引用

ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Taghizadeh, Mohammad J. Garner, Philip N. Bourlard, Herve Idiap Res Inst Lausanne Switzerland Idiap Res Inst EPFL Lausanne Switzerland

ISBN: (纸本)9781467310710

Spatial filtering is the fundamental characteristic of microphone array based signal acquisition, which plays an important role in applications such as speech enhancement and distant speech recognition. In the array processing literature, this property is formulated upon beam-pattern steering and it is characterized for narrowband signals. This paper proposes to characterize the microphone array broadband beam-pattern based on the average output of a steered beamformer for a broadband spectrum. Relying on this characterization, we derive the directivity beam-pattern of delay-and-sum and superdirective beamformers for a linear as well as a circular microphone array. We further investigate how the broadband beam-pattern is linked to speech recognition feature extraction;hence, it can be used to evaluate distant speech recognition performance. The proposed theory is demonstrated with experiments on real data recordings.

关键词： Broadband beam-pattern Delay-and-sum beamformer Superdirective beamformer Distant speech recognition

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Source counting in real-time sound source localization using a circular microphone array

Source counting in real-time sound source localization using...

引用

ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Pavlidi, Despoina Griffin, Anthony Puigt, Matthieu Mouchtaris, Athanasios FORTH ICS GR-70013 Iraklion Crete Greece

ISBN: (纸本)9781467310710

Recently, we proposed an approach inspired by Sparse Component Analysis for real-time localization of multiple sound sources using a circular microphone array. The method was based on identifying time-frequency zones where only one source is active, reducing the problem to single-source localization for these zones. A histogram of estimated Directions of Arrival (DOAs) was formed and then processed to obtain improved DOA estimates, assuming that the number of sources was known. In this paper, we extend our previous work by proposing three different methods for counting the number of sources by looking for prominent peaks in the derived histogram based on: (a) performing a peak search, (b) processing an LPC-smoothed version of the histogram, (c) employing a matching pursuit-based approach. The third approach is shown to perform very accurately in simulated reverberant conditions and additive noise, and its computational requirements are very small.

关键词： Additive noise

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Dictionary Based Reconstruction and Classification of Randomly Sampled sensor Network Data

Dictionary Based Reconstruction and Classification of Random...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Tsagkatakis, Grigorios Tsakalides, Panagiotis Fdn Res & Technol Hellas FORTH Inst Comp Sci Iraklion Greece

ISBN: (纸本)9781467310710

In this paper, we propose a method for recovering and classifying WSN data while minimizing the number of samples that need to be acquired, processed, and transmitted. The problem is formulated according to the recently proposed framework of Matrix Completion (MC), which asserts that a low rank matrix can be recovered from a small number of randomly sampled entries. The application of MC in WSN data is motivated by the assumption that sensory data exhibit intra-sensor correlations and that these data can be represented using known examples. We formulate the problem as that of recovering the low rank measurement matrix by encoding the contributions of known examples, the dictionary elements, for reconstructing and classifying the data. Experimental results using artificial data suggest that the proposed scheme is able to accurately reconstruct and classify the sensory data from a small number of measurements.

关键词： sensor networks

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Hierarchical Particle Filtering for Target Tracking in Multi-Modal sensor Networks

Hierarchical Particle Filtering for Target Tracking in Multi...

引用

ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Chavali, Phani Nehorai, Arye Washington Univ Preston M Green Dept Elect & Syst Engn St Louis MO 63130 USA

ISBN: (纸本)9781467310710

We propose a filtering method, called hierarchical particle filtering, for multi-modal sensor networks in which the unknown state vector is observed, through the measurements, in a hierarchical fashion. We partition the state space and the measurement space into lower dimensional subspaces. At each stage, we find an estimate of one partition using the measurements from the corresponding partition, and the information from the previous stages. We use hierarchical particle filtering for joint initiation, termination and tracking of multiple targets using multi-modal measurements. Numerical simulations demonstrate that the proposed filtering method accurately identifies the number and the categories of targets, and produces a lower mean-squared error (MSE) compared to the MSE obtained using a standard particle filter.

关键词： Particle filters metric space Target tracking Hierarchical sensor networks state space

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Performance of a Practical Two-Step Detector for Non-Fluctuating Targets

Performance of a Practical Two-Step Detector for Non-Fluctua...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Scharrenbroich, Max Zatman, Michael Balan, Radu QinetiQ N Amer Reston VA 22102 USA Univ Maryland Dept Math College Pk MD 20742 USA

ISBN: (纸本)9781467310710

In this paper we analyze a two-step detection scheme for use in distributed sensor systems (e. g. statistical MIMO radar). The scheme arises when a data rate restriction forces each of the distributed systems to censor their detection statistics before sharing. We present the Neyman-Pearson (NP) two-step detection rule for a non-fluctuating target model (Swerling 0), which is non-linear and requires a priori knowledge of the target SNR. We then analyze the performance of a practical two-step detection rule under the non-fluctuating target model.

关键词： Distributed Systems Scheme MIMO radar distributed sensors sensor systems

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Optimal Precoding Design for Decentralized Detection of Deterministic signals

Optimal Precoding Design for Decentralized Detection of Dete...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Fang, Jun Li, Hongbin Chen, Zhi Li, Shaoqian Univ Elect Sci & Technol China Natl Key Lab Commun Chengdu 610054 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

ISBN: (纸本)9781467310710

We consider a decentralized detection problem in a power-constrained wireless sensor networks (WSNs), in which a number of sensor nodes collaborate to detect the presence of a deterministic vector signal. The signal to be detected is assumed known a priori. Each sensor conducts a local linear processing to convert its observations into one or multiple messages. The messages are conveyed to the fusion center (FC) by an uncoded amplify-and-forward scheme, where a global decision is made. Given a total network transmit power constraint, we investigate the optimal linear processing strategy for each sensor. Our study finds that the optimal linear precoder has the form of a matched filter. Depending on the channel characteristics, one or multiple versions of the filtered/compressed message should be reported to the FC.

关键词： Decentralized detection precoding design wireless sensor networks

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Robust signal Detection by Using The EEF

Robust Signal Detection by Using The EEF

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Kay, Steven Ding, Quan Rangaswamy, Muralidhar Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Wright Patterson AFB US Air Force Res Lab Columbus OH USA

ISBN: (纸本)9781467310710

In detection theory, the optimal Neyman-Pearson rule applies when the characteristics of the signal and the noise are completely known. However, in many practical scenarios such as multipath or moving targets, only partial knowledge of the signal can be obtained. In this paper, we examine the case when the alternative hypothesis has multiple candidate models, and apply the multimodal sensor integration technique based on the exponentially embedded family to detection. It is shown that our method is asymptotically optimal as it converges to the true underlying model. Furthermore, this method is computationally efficient. We also compare the proposed method with existing classifier combining rules by simulations.

关键词： Detection exponentially embedded family multimodal sensor integration robustness

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Distributed Binary Geometric sensor arrays for Low-Data-Throughput Human Gait Biometrics

Distributed Binary Geometric Sensor Arrays for Low-Data-Thro...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Yue, Tianyao Hao, Qi Brady, David J. Duke Univ Dept Elect & Comp Engn Box 90291 Durham NC 27708 USA Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA

ISBN: (纸本)9781467310710

We present a novel sensing paradigm of measuring human gait. The goal of the research is to achieve low-cost gait biometrics systems with minimum data throughput for various sensing modalities. The binary measurements of the system are achieved by using both (1) periodic and (2) pseudo-random sampling structures. As a result, either static or dynamic gait features can be estimated from a one-bit data stream. The simulation results demonstrate the gait information acquisition capability of the proposed binary sensing technology.

关键词： Biometrics

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sensor Network Distributed Computation for Direct Position Determination

Sensor Network Distributed Computation for Direct Position D...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Pourhomayoun, Mohammad Fowler, Mark L. SUNY Binghamton Dept Elect & Comp Engn Binghamton NY 13902 USA

ISBN: (纸本)9781467310710

Classical localization systems based on TDOA/FDOA use a two-stage estimation approach. In the first stage, pairs of sensors share data to estimate TDOA/FDOA. Then, the extracted TDOA/FDOA measurements are used to estimate the emitter location. In some recently published methods, an optimal single-stage approach named Direct Position Determination (DPD) has been proposed to improve the position estimation accuracy. However, unlike the classical two-stage method where the TDOA/FDOA estimation can be distributed across all sensors, DPD processes all the received signals together at a single sensor node. However, when sensors have limited computational capabilities it is desirable to distribute the computation across all sensors. Furthermore, concentrating all the processing into a single node makes the location system less robust to the loss of sensors. In this paper, we develop a distributed localization method with the goal of reducing the computational load on each sensor and increasing the reliability of the system.

关键词： Time difference of arrival

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Compressed Sensing Radar Surveillance Networks

Compressed Sensing Radar Surveillance Networks

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Schmidt, Aurora Harley, Joel B. Moura, Jose M. F. Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781467310710

We study the problem of sensor fusion in a simplified radar surveillance application. A potentially large number of narrowband radars with isotropic antennas monitor a two-dimensional area for an unknown number of targets. We use techniques from compressive sensing to distribute efficient projections of network observations, allowing for reconstruction of the target scene using a single snapshot of sensor data. We avoid the use of a fusion node, allowing all radars to individually estimate target locations after iterative communication with neighboring sensors. We study the robustness of the discretization of continuous target locations, comparing estimation performance of basis pursuit reconstruction methods to a sparse estimator based on a model-robust formulation. We test the approach on simulated scenarios, showing tradeoffs in the resolution of target localization as well as the communication bandwidths required for this inter-radar cooperation scheme.

关键词： distributed radar sensor fusion consensus compressed sensing noise-aware basis pursuit model robust estimation

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