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2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012

作者： Wang, Jianglin Ji, An Johnson, Michael T. Speech and Signal Processing Laboratory Department of Electrical and Computer Engineering Marquette University Milwaukee WI United States

ISBN: (纸本)9781467301718

This paper describes a unique cross-phoneme speaker identification experiment, using deliberately mismatched phoneme sets for training and testing. The underlying goal is to identify features that represent broad individually unique characteristics rather than those that represent phonetic differences, as are more typical of modern speaker identification and verification systems. A wide range of features are proposed and evaluated within this context using a Gaussian Mixture Model framework. The results show that log-area ratio has better phonetic independence than MFCCs, that residual phase carries substantial speaker information, and identifies several other features that also have usefulness for speaker identification independent of phonetic content. © 2012 IEEE.

关键词： Gaussian distribution

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Modified full abundance-constrained spectral unmixing

Modified full abundance-constrained spectral unmixing

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High-Performance Computing in Remote Sensing II

作者： Wong, Englin Chang, Chein-I Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780819492791

Abundance fully constrained least squares (FLCS) method has been widely used for spectral unmixing. A modified FCLS (MFCLS) was previously proposed for the same purpose to derive two iterative equations for solving fully abundance-constrained spectral unmixing problems. Unfortunately, its advantages have not been recognized. This paper conducts a comparative study and analysis between FCLS and MFCLS via custom-designed synthetic images and real images to demonstrate that while both methods perform comparably in unmixing data, MFCLS edges out FCLS in less computing time. © 2012 SPIE.

关键词： Iterative methods

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Correction to “An Azimuth-Dependent Phase Gradient Autofocus (APGA) Algorithm for Airborne/Stationary BiSAR Imagery” [Nov 13 1290-1294]

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IEEE Geoscience and Remote Sensing Letters 2013年第6期10卷 1622-1622页

作者： Song Zhou Mengdao Xing Xiang-Gen Xia Yachao Li Lei Zhang Zheng Bao National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Department of Electrical and Computer Engineering University of Delaware Newark DE USA

In the above paper (ibid., vol. 10, no, 6, pp. 1290-1294, Nov. 2013), there is an error in equation (5). The correction is presented here.

关键词： Azimuth Synthetic aperture radar Geophysical measurements Airborne radar

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Tracking articulator movements using orientation measurements

Tracking articulator movements using orientation measurement...

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2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012

作者： Ji, An Johnson, Michael T. Berry, Jeffrey Marquette University Department of Electrical and Computer Engineering Speech and Signal Processing Laboratory P. O. Box 1881 Milwaukee WI 53201-1881 United States

ISBN: (纸本)9781467301718

This paper introduces a new method to track articulator movements, specifically jaw position and angle, using 5 degree of freedom (5 DOF) orientation data. The approach uses a quaternion rotation method to accomplish this jaw tracking during speech using a single senor on the mandibular incisor. Data were collected using the NDI Wave Speech Research System for one pilot subject with various speech tasks. The degree of jaw rotation from the proposed approach is compared with traditional geometric calculation. Results show that the quaternion based method is able to describe jaw angle trajectory and gives more accurate and smooth estimation of jaw kinematics. © 2012 IEEE.

关键词： Degrees of freedom (mechanics)

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Features for Phoneme Independent Speaker Identification

Features for Phoneme Independent Speaker Identification

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International Conference on Audio, Language and Image Processing

作者： Jianglin Wang An Ji Michael T. Johnson Speech and Signal Processing Laboratory Department of Electrical and Computer Engineering Marquette University Speech and SignalProcessing Laboratory Department of Electrical and Computer Engineering Marquette University

ISBN: (纸本)9781467301732

关键词： speaker recognition Mel frequency cepstral coefficient phonetics verification system

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Tracking articulator movements using orientation measurements

Tracking articulator movements using orientation measurement...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： An Ji Michael T. Johnson Jeffrey Berry Department of Electrical and Computer Engineering Speech and Signal Processing Laboratory Marquette University Milwaukee WI USA

关键词： Speech Quaternions Tracking Vectors Sensor phenomena and characterization Acoustics

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Noise Robust Radar HRRP Target Recognition Based on Multitask Factor Analysis With Small Training Data Size

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IEEE Transactions on signal Processing 2012年第7期60卷 3546-3559页

作者： Lan Du Hongwei Liu Penghui Wang Bo Feng Mian Pan Zheng Bao Department of Electrical and Computer Engineering Duke University Durham NC USA TheNational Laboratory of Radar Signal Processing Xidian University Xi'an China

A factor analysis model based on multitask learning (MTL) is developed to characterize the FFT-magnitude feature of complex high-resolution range profile (HRRP), motivated by the problem of radar automatic target recognition (RATR). The MTL mechanism makes it possible to appropriately share the information among samples from different target-aspects and learn the aspect-dependent parameters collectively, thus offering the potential to improve the overall recognition performance with small training data size. In addition, since the noise level of a test sample is usually different from those of the training samples in the real application, another contribution is that the proposed framework can update the noise level parameter in the FA model to adaptively match that of the received test sample. Efficient inference is performed via variational Bayesian (VB) for the proposed hierarchical Bayesian model, and encouraging results are reported on the measured HRRP dataset with small training data size and under the test condition of low signal-to-noise ratio (SNR).

关键词： Data models Hidden Markov models Feature extraction Radar Noise robustness Analytical models Target recognition

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The impact of ADC nonlinearity in a mixed-signal compressive sensing system for frequency-domain sparse signals

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Physical Communication 2012年第2期5卷 196-207页

作者： Yu, Zhuizhuan Zhou, Jun Ramirez, Mario Hoyos, Sebastian Sadler, Brian M. Analog and Mixed Signal Center Department of Electrical and Computer Engineering Texas A and M University College Station TX 77843-3128 United States Army Research Laboratory AMSRD-ARL-CI-CN Adelphi MD 20783 United States Texas Instrument 12500 TI Blvd MS 8755 Dallas TX 75243 United States

Compressive sensing (CS) holds new promises for the digitization of wideband. frequency-domain sparse signals at sub-Nyquist rate sampling without compromising the reconstruction quality. In this paper, the impact of ADC nonlinearity in a CS receiver for frequency-domain sparse signals is investigated. In a mixed-signal CS system, signals are randomized before sampling. The signal spectrum at each building block in the mixed-signal CS system is analyzed and compared to a conventional Nyquist-rate sampling system. It is shown that the signal randomization in a mixed-signal CS system is able to spread the spurious energy due to ADC nonlinearity along the signal bandwidth, rather than the concentration of harmonic distortion on a few frequencies as it is the case for a conventional ADC. As a result, this paper shows that a significant ADC SFDR (Spurious Free Dynamic Range) improvement is achieved in a CS receiver when processing sparse signals. Simulation results are reported which are in good agreement with the qualitative analysis. © 2011.

关键词： Compressed sensing

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A PLL based adaptive power line interference filtering from ECG signals

A PLL based adaptive power line interference filtering from ...

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International Symposium on Artificial Intelligence and signal Processing (AISP)

作者： M. M. Zeinali Zadeh S. Niketeghad R. Amirfattahi Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

One of the challenges for electrocardiogram (ECG) signals extraction is additive power line interference (PLI) which contains 50Hz or 60Hz base frequency and several harmonics. Variety of fixed-frequency and adaptive notch filter structures are proposed for power line interference suppression. Because of frequency varying behavior of power line interference, fixed frequency filters suffer from the problem of low noise attenuation rate when the interference frequencies drift from their nominal values, and also signal amplitude and phase destruction around interference frequencies due to relatively high notching bandwidth. On the other hand adaptive filters are given high calculation costs. In this paper we propose a real-time, low calculation cost adaptive notch filter, using phase-locked loop (PLL). Experimental results show that in extremely noisy environments where input SNR is around - 20dB, average SNR enhancement of proposed method is approximately 40dB. This value is also as good as 35dB for -10dB input SNR.

关键词： IIR filters Power harmonic filters Adaptive filters Electrocardiography Interference signal to noise ratio Bandwidth

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Adaptive image compression using sparse dictionaries

Adaptive image compression using sparse dictionaries

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International Conference on Systems, signals and Image Processing, IWSSIP

作者： Inbal Horev Ori Bryt Ron Rubinstein Department of Electrical Engineering Signal and Image Processing Laboratory Haifa Israel Department of Computer Science Geometric Image Processing Laboratory Haifa Israel

Transform coding is a widely used image compression technique, where entropy reduction can be achieved by decomposing the image over a dictionary which provides compaction. Existing algorithms, such as JPEG and JPEG2000, utilize fixed dictionaries which are shared by the encoder and decoder. Recently, works utilizing content-specific dictionaries show promising results by focusing on specific classes of images and using highly specialized dictionaries. However, such approaches lose the ability to compress arbitrary images. In this paper we propose an input-adaptive compression approach, which encodes each input image over a dictionary specifically trained for it. The scheme is based on the sparse dictionary structure, whose compact representation allows relatively low-cost transmission of the dictionary along with the compressed data. In this way, the process achieves both adaptivity and generality. Our results show that although this method involves transmitting the dictionary, it remains competitive with the JPEG and JPEG2000 algorithms.

关键词： Dictionaries Image coding Transform coding PSNR Sparse matrices Encoding Bit rate

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