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arXiv 2021年

作者： Liu, Ting Wang, Wenwu Zhang, Xiaofei Yan, Jingwen Guo, Yina The Department of Electronics and Communication Engineering Taiyuan University of Science and Technology Taiyuan030024 China The Centre for Vision Speech and Signal Processing University of Surrey Guildford SurreyGU2 7XH United Kingdom The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Technology

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on separating two sources and have weak generalization performance. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to realize one-to-multiple cross-domain mapping. This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model. Besides, one-to-multiple datasets are created which including the mixtures and corresponding sources for this study. The experiment was carried out on four different datasets and tested with signals mixed in different proportions. Experimental results show that the proposed algorithm can achieve high performance in peak signal-to-noise ratio (PSNR) and correlation, which outperforms state-of-the-art algorithms. © 2021, CC BY.

关键词： Mapping

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Data-adaptive algorithms for signal detection in impulsive noise modeled as a subGaussian, alpha-stable process

Data-adaptive algorithms for signal detection in impulsive n...

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IEEE Workshop on Statistical signal and Array processing

作者： G.A. Tsihrintzis C.L. Nikias Communication Systems Laboratory Department of Electrical Engineering University of Virginia Charlottesville VA USA Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

We address the problem of coherent detection of a signal embedded in heavy-tailed noise modeled as a subGaussian, alpha-stable process. We assume that the signal is a complex-valued vector of length L, known only within a multiplicative constant. The dependence structure of the noise, i.e., the underlying matrix of the sub Gaussian process, is not known. The intent is to implement a generalized likelihood ratio detector which employs robust estimates of the unknown noise underlying matrix and the unknown signal strength. The performance of the proposed adaptive detector is compared to that of an adaptive matched filter that uses Gaussian estimates of the noise underlying matrix and the signal strength and is found to be clearly superior. The proposed new algorithms an evaluated via Monte-Carlo simulation.

关键词： signal detection Interference signal processing signal processing algorithms Detectors Gaussian noise signal design Probability distribution image processing Noise robustness

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Multi-Modal Human Verification Using Face and Speech

Multi-Modal Human Verification Using Face and Speech

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IEEE International Conference on Computer Vision Systems (ICVS)

作者： Changhan Park Joonki Paik Taewoong Choi Soonhyob Kim Youngouk Kim J. Namkung Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Multimedia Signal Processing Laboratory Kwangwoon University Seoul South Korea Precision Machinery Center Korea Electronics and Technology Institute Puchon Gyeonggi South Korea Intelligent Image Communication Laboratory Department of Computer Engineering Kwangwoon University Seoul South Korea

In this paper, we propose a personal verification method using both face and speech to improve the rate of single biometric verification. False acceptance rate (FAR) and false rejection rate (FRR) have been a fundamental bottleneck of real-time personal verification. The proposed multimodal biometric method is to improve both verification rate and reliability in real-time by overcoming technical limitations of single biometric verification methods. The proposed method uses principal component analysis (PCA) for face recognition and hidden markov model (HMM) for speech recognition. It also uses fuzzy logic for the final decision of personal verification. Based on experimental results, the proposed system can reduce FAR down to 0.0001%, which provides that the proposed method overcomes the limitation of single biometric system and provides stable personal verification in real-time.

关键词： Humans Speech Face detection Face recognition Biometrics Principal component analysis Feature extraction Hidden Markov models image edge detection Laboratories

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An Insight into the Estimation of MIMO Propagation Parameters using the Sage Algorithm

An Insight into the Estimation of MIMO Propagation Parameter...

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The Second European Conference on Antennas and Propagation, EuCAP 2007

作者： M. Matthaiou N. Razavi-Ghods D.I. Laurenson S. Salous Institute for Digital Communications Joint Research Institute for Signal and Image Processing University of Edinburgh UK Cavendish Laboratory University of Cambridge Cambridge U.K. Centre for Communication Systems University of Durham Durham UK

In this contribution, we provide a thorough review of the application of SAGE (space-alternating generalised expectation-maximisation) algorithm to the problem of parameter estimation for MIMO (multiple-input multiple-output) systems. It is well established that the SAGE algorithms yields a superior performance compared to other estimation algorithms in terms of accuracy and robustness. For our investigation, we consider both the time and frequency domain evaluations of the algorithm since each domain requires a different signal model.

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The world of mushrooms - A transdisdplinary approach to human-computer interaction with ambient intelligence

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NTT Technical Review 2006年第12期4卷 17-24页

作者： Maeda, Eisaku Minami, Yasuhiro Miyoshi, Masato Sawaki, Minako Sawada, Hiroshi Nakamura, Atsushi Yamato, Junji Yamada, Takeshi Higashinaka, Ryuichiro NTT Communication Science Laboratories Atsugi-shi 243-0198 Japan Ambient Intelligence Research Group Media Intormation Laboratory NTT Communication Science Laboratories IEEE IEICE IPSJ Signal Processing Research Group Media Information Laboratory NTT Communication Science Laboratories ASJ Audio Engineering Society Recognition Research Group Media Information Laboratory NTT Communication Science Laboratories Institute of Image Information and Television Engineers Robotics Society of Japan Emergent Learning and Systems Research Group Innovative Communication Laboratory NTT Communication Science Laboratories ACM Scheduling Society of Japan Knowledge Processing Research Group Innovative Communication Laboratory NTT Communication Science Laboratories Association for Computational Linguistics

This paper provides an update to the January 2006 letter on "ambient intelligence", describing this year's achievements and the latest demonstration systems. The Ambient Intelligence Project (also known by the code name project Mushroom) aims to bridge the boundaries between technological fields and thus cover the entire field of communication science.

关键词： Human computer interaction

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image Fusion Algorithm Based On Orientation Information Motivated Pulse Coupled Neural Networks

Image Fusion Algorithm Based On Orientation Information Moti...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.6

作者： Xiaobo Qu Changwei Hu Jingwen Yan Department of Communication Engineering Xiamen University Xiamen Fujian China Department of Software Engineering Xiamen University Xiamen Fujian China Key Laboratory of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou Guangdong China

Pulse Coupled Neural Networks (PCNN) is a visual cortex-inspired neural networks and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for image processing and successfully employed in image fusion. However, in most PCNN-based fusion algorithms, only single pixel value is input to motivate PCNN neuron. This is not effective enough because humans are often sensitive to features, not only pixel value. In this paper, novel orientation information is considered as features to motivate PCNN. Visual observation and objective performance evaluation criteria demonstrate that the proposed algorithm outperforms typical wavelet-based, lapacian pyramid transform-based and PCNN-based fusion algorithms.

关键词： image fusion Pulse Coupled Neural Networks Orientation information Wavelet transform image processing

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Speech extraction based on ICA and audio-visual coherence

Speech extraction based on ICA and audio-visual coherence

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International Symposium on signal processing and Its Applications (ISSPA)

作者： D. Sodoyer L. Girin C. Jutten J.-L. Schwartz Speech Communication Institute (ICP) CMS UMR 5009 INPG Université Stendhal Grenoble France Speech Communication Institute (ICP) CNRS UMR 5009 INPG Université Stendhal Grenoble France Image and Signal Processing Laboratory (LIS) CNRS UMR 5083. INPG UJF Grenoble France

We present a new approach to the source separation problem for multiple speech signals. Using the extra visual information of the speaker's face, the method aims to extract an acoustic speech signal from other acoustic signals by exploiting its coherence with the speaker's lip movements. We define a statistical model of the joint probability of visual and spectral audio input for quantifying the audio-visual coherence. Then, separation can be achieved by maximising this joint probability. Experiments on additive mixtures of 2, 3 and 5 sources show that the algorithm performs well, and systematically better than the classical BSS algorithm JADE.

关键词： Independent component analysis Source separation Finite impulse response filter Speech enhancement Filter bank Laboratories Data mining Coherence Probability Additive noise

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image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain

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Acta Automatica Sinica 2008年第12期34卷 1508-1514页

作者： Xiao-Bo QU Jing-Wen YAN Hong-Zhi XIAO Zi-Qian ZHU Department of Communication Engineering Xiamen University Xiamen 361005 P.R.China Key Laboratory of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou 515063 P. R. China Research Institute of Chinese Radar Electronic Equipment Wuxi 214063 P.R.China

Nonsubsampled contourlet transform (NSCT) provides flexible multiresolution, anisotropy, and directional expansion for images. Compared with the original contourlet transform, it is shift-invariant and can overcome the pseudo-Gibbs phenomena around singularities. Pulse coupled neural networks (PCNN) is a visual cortex-inspired neural network and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for image processing and successfully employed in image fusion. In this paper, NSCT is associated with PCNN and used in image fusion to make full use of the characteristics of them. Spatial frequency in NSCT domain is input to motivate PCNN and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed algorithm outperforms typical wavelet-based, contourlet-based, PCNN-based, and contourlet-PCNN-based fusion algorithms in terms of objective criteria and visual appearance.

关键词： Contourlet pulse coupled neural networks (PCNN) wavelet image fusion multiscale transform

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Using audiovisual speech processing to improve the robustness of the separation of convolutive speech mixtures

Using audiovisual speech processing to improve the robustnes...

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IEEE Workshop on Multimedia signal processing

作者： B. Rivet L. Girin C. Jutten J.-L. Schwartz Speech Commun. Inst. Grenoble Nat. Polytech Inst. France Speech Communication Institute (ICP) National Polytechnic Institute of Grenoble Grenoble France Image and Signal Processing Laboratory(LIS) National Polytechnic Institute of Grenoble Grenoble France

Looking at the speaker's face seems useful in hearing better a speech signal and extract it from the competing sources before identification. In this paper, we present a novel algorithm plugging audiovisual coherence of speech signals, estimated by statistical tools, on audio blind source separation (BSS) algorithms in the difficult case of convolutive mixtures. The algorithm mainly works in the frequency (transform) domain, where the convolutive mixture becomes an additive mixture for each frequency channel. Frequency by frequency separation is made by an audio BSS algorithm, and the audiovisual information is used to solve the standard source permutation problem at the output of the separation stage, for each frequency. The proposed method is shown to be efficient in the case of 2 /spl times/ 2 convolutive mixtures.

关键词： Speech processing Robustness Source separation Filters Speech enhancement signal processing Frequency Blind source separation Oral communication Iterative closest point algorithm

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Multi-modal human verification using face and speech

Multi-modal human verification using face and speech

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Fourth IEEE International Conference on Computer Vision Systems, ICVS'06

作者： Park, Changhan Choi, Taewoong Kim, Youngouk Kim, Soonhyob Namkung, Jaechan Paik, Joonki Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University 221 Huksuk-dong Tongjak-Ku Seoul 156-756 Korea Republic of Multimedia Signal Processing Laboratory Department of Computer Engineering Kwangwoon University 447-1 Wolge-dong Nowon-Gu Seoul 139-701 Korea Republic of Intelligent Image Communication Laboratory Department of Computer Engineering Kwangwoon University 447-1 Wolge-dong Nowon-Gu Seoul 139-701 Korea Republic of Precision Machinery Center Korea Electronics Technology Institute 401-402 B/D 193 Yakdae-dong Wonmi-Gu Puchon Kyunggido 420-140 Korea Republic of

ISBN: (纸本)0769525067

关键词： Face recognition

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