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IEEE/SP Workshop on Statistical signal processing (SSP)

作者： Tania Habib Muhammad Tufail Signal Processing and Speech Communication Laboratory Graz University of Technology Austria Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences Pakistan

In this paper we have investigated the effects of acoustic feedback on multi-channel ANC system. The presence of strong acoustic feedback degrades the convergence speed of the ANC filters, and in worst case the multi-channel ANC system may become unstable. The acoustic feedback path can be modeled offline, but in most applications it is time varying, hence it needs to be modeled online when ANC is in operation. Here, we propose a Variable Step Size based method for multi-channel ANC systems with online acoustic feedback path modeling. The computer simulations are carried out, which demonstrate the effectiveness of the proposed method, in comparison with the existing methods for 1 times 2 times 2 ANC system.

关键词： Feedback Acoustic noise Active noise reduction Control system synthesis Degradation Convergence Filters Acoustic applications Application software computer simulation

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Decision Level Fusion of Colour Histogram Based Classifiers for Clustering of Mouth Area Images

Decision Level Fusion of Colour Histogram Based Classifiers ...

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International Conference on Digital Image processing, ICDIP

作者： Fahimeh Salimi Mohammad T. Sadeghi Signal Processing Research Laboratory Department of Electrical and Computer Engineering Engineering Research Centre Yazd University Yazd Iran

It is well known that in many situations combining diverse classifiers can improve the performance of a classification system. In this paper, a new histogram based lip segmentation technique is proposed considering local kernel histograms in different illumination invariant colour spaces. The histogram is computed in local areas using two Gaussian kernels; one in the colour space and the other in the spatial domain. Using the estimated histogram, the posterior probability associated to non-lip class is then computed for each pixel. This process is performed considering different colour spaces. A weighted averaging method is then used for fusing the posterior probability values. As the result a new score is obtained which is used for labeling the pixels as lip or non-lip. The advantage of the proposed method is that the segmentation process is totally unsupervised. So, the method is robust against different variations such as variation in lip shape, skin colour, facial hair, illumination, etc. Moreover, an improved performance is achieved by fusing colour information.

关键词： Histograms Mouth Kernel Lighting Diversity reception Image segmentation Color Labeling Robustness Shape

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Incremental learning in nonstationary environments with controlled forgetting

Incremental learning in nonstationary environments with cont...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ryan Elwell Robi Polikar Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering Department Rowan University Glassboro NJ USA

We have recently introduced an incremental learning algorithm, called Learn ++ .NSE, designed for Non-Stationary Environments (concept drift), where the underlying data distribution changes over time. With each dataset drawn from a new environment, Learn ++ .NSE generates a new classifier to form an ensemble of classifiers. The ensemble members are combined through a dynamically weighted majority voting, where voting weights are determined based on classifiers' age-adjusted accuracy on current and past environments. Unlike other ensemble-based concept drift algorithms, Learn ++ .NSE does not discard prior classifiers, allowing potentially cyclical environments to be learned more effectively. While Learn ++ .NSE has been shown to work well on a variety of concept drift problems, a potential shortcoming of this approach is the cumulative nature of the ensemble size. In this contribution, we expand our analysis of the algorithm to include various ensemble pruning methods to introduce controlled forgetting. Error or age-based pruning methods have been integrated into the algorithm to prevent potential out-voting from irrelevant classifiers or simply to save memory over an extended period of time. Here, we analyze the tradeoff between these precautions and the desire to handle recurring contexts (cyclical data). Comparisons are made using several scenarios that introduce various types of drift.

关键词： Neutron spin echo Algorithm design and analysis Voting

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Comparison between noise reduction algorithms for speech signals using auditory masking effects

Comparison between noise reduction algorithms for speech sig...

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IEEE GCC Conference & Exhibition

作者： Mehdi Yektaeian Rassoul Amirfattahi Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Auditory masking is aggressively exploited by algorithms used for the lossy compression of audio signals. In compression of audio signals, the intent is to hide the noise introduced by the coding below the masking threshold, thus making the noise inaudible. This will render the coding process transparent, enabling better compression without audible degradation of the signal. In this article, we show that using masking properties of the hearing system allows for improved noise reduction. A novel method for noise reduction in speech signals is proposed. This method is shown to outperform non-auditory based methods, and compares well with other perceptually motivated noise reduction methods. It is found that the proposed method, Soulodre's PNRF combined with the ITU's PEAQ auditory model, have more musical noise but less signal distortion than the method proposed by Tsoukalas, which obtain marginally better results in informal testing results.

关键词： signal to noise ratio Noise reduction speech speech enhancement Masking threshold

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Real-time processing of simplex growing algorithm

Real-time processing of simplex growing algorithm

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2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009

作者： Wu, Chao-Cheng Chang, Chein-I. Ren, Husen Chang, Yang Lang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Center for Space and Remote Sensing Research National Central University Jhong-Li Tao-Yuan Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781424433957

Simplex growing algorithm (SGA) was recently developed as an alternative to the N-finder algorithm (N-FINDR) which is shown to be a promising endmember extraction technique. This paper further extends the SGA to a real-time processing algorithm, referred to as real-time SGA (RT SGA) that can effectively address four major issues arising in practical implementation for N-FINDR, (1) use of random initial endmembers which causes inconsistent final results, (2) very high computational complexity which results from an exhaustive search for finding all endmembers simultaneously, (3) requirement of dimensionality reduction because of enormous data volumes to be processed and (4) lack of real-time capability. ©2009 IEEE.

关键词： Extraction

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signal processing for neural spike trains

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Computational intelligence and neuroscience 2010年第1期2010卷 698751页

作者： Theodore W Berger Zhe Sage Chen Andrzej Cichocki Karim G Oweiss Rodrigo Quian Quiroga Nitish V Thakor Department of Biomedical Engineering Viterbi School of Engineering University of Southern California Los Angeles CA 90089 USA Neuroscience Statistics Research Laboratory Massachusetts Institute of Technology Cambridge MA 02139 USA Laboratory for Advanced Brain Signal Processing RIKEN Brain Science Institute Wako-shi Saitama 351-0198 Japan Department of Electrical and Computer Engineering Michigan State University MI 48824 USA Department of Engineering University of Leicester Leicester LE1 7RH UK School of Medicine Johns Hopkins University Baltimore MD 21205 USA

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Kernel-based Linear Spectral Mixture Analysis for hyperspectral image classification

Kernel-based Linear Spectral Mixture Analysis for hyperspect...

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Workshop on Hyperspectral Image and signal processing: Evolution in Remote Sensing, WHISPERS

作者： Keng-Hao Liu Englin Wong Chein-I Chang Remote Sensing Signal and Image Processing Laboratory the Department of Computer Science and Electrical Engineering Department University of Maryland Baltimore MD USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

Linear spectral mixture analysis (LSMA) has been widely used in remote sensing community. Recently, kernel-based approaches have received considerable interest in hyperspectral image analysis where nonlinear kernels are used to resolve the issue of nonlinear separability in classification. This paper extends the LSMA to kernel-based LSMA where three least squares-based LSMA techniques, least squares orthogonal subspace projection (LSOSP), non-negativity constrained least squares (NCLS) and fully constrained least squares (FCLS) are extended to their kernel counterparts, KLSOSP, KNCLS and KFCLS.

关键词： Image analysis Spectral analysis Hyperspectral imaging Image classification Least squares methods Hyperspectral sensors Kernel Subspace constraints Remote sensing Image resolution

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Multimodal speaker localization from omnidirectional videos

Multimodal speaker localization from omnidirectional videos

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European signal processing Conference (EUSIPCO)

作者： Pascal Reuse Mihai Gurban Ivar Austvoll Jean-Philippe Thiran Ecole Polytechnique Federale de Lausanne Lausanne Signal Processing Laboratory 5 Switzerland Department of Electrical and Computer Engineering University of Stavanger Stavanger Norway

The use of omnidirectional cameras for videoconferencing promises to simplify the hardware setup necessary for large groups of participants. We investigate the use of a multimodal speaker detection algorithm on audio-visual sequences captured with such a camera, in particular, an algorithm that uses the audio energy together with the optical flow. We analyze several types of optical flow methods to determine the one which is appropriate to the omnidirectional context.

关键词： Optical filters Image motion analysis computer vision Optical imaging Optical distortion Cameras Integrated optics

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Dimensionality reduction of SIFT using PCA for object categorization

Dimensionality reduction of SIFT using PCA for object catego...

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IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Nattachai Watcharapinchai Supavadee Aramvith Supakorn Siddhichai Sanparith Marukatat Digital Signal Processing Research Laboratory Department of Electrical Engineering Chulalongkom University Bangkok Thailand National Electronics and Computer Technology Center Pathumthani Thailand

The problem of automatic object categorization is investigated under the proposed bag of feature object categorization framework. The framework consists of feature detection and representation which uses the scale invariant feature transform (SIFT) as local feature and bag of feature model to represent the image. Learning process utilizes k-NN (k-nearest neighbour). In this paper, we propose the dimensionality reduction of SIFT using principal component analysis (PCA) on each object category to reduce computational complexity and memory requirement during training process. Experimental results show that our proposed technique can reduce the dimension of SIFT up to around 80% with the same average precision compared to baseline technique without our proposed method.

关键词： Principal component analysis computer vision Histograms Digital signal processing Computational complexity Video compression Testing Training data Vocabulary Feature extraction

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Achieving Close-Capacity Performance with Simple Concatenation Scheme on Multiple-Antenna Channels

Achieving Close-Capacity Performance with Simple Concatenati...

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GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

作者： Nghi H. Tran Tho Le-Ngoc Tad Matsumoto Ha H. Nguyen Department of Electrical & Computer Engineering McGill University Montreal QUE Canada Information Theory and Signal Processing Laboratory School of Information Science Japan Advanced Institute of Science and Technology Japan Department of Electrical & Computer Engineering University of Saskatchewan Saskatoon SAS Canada

This paper proposes a simple yet capacity-approaching concatenation of a mixture of short memory length convolutional codes and simple rate-1 block code followed by either complex 1-dimensional (1-D) anti-Gray or Gray mapping over multiple antenna channels with quadrature phase-shift keying (QPSK). By interpreting rate-1 code together with 1-D mapping as a multi-D mapping employed over multiple transmit antennas, the error performance is analyzed in two regions, the error-floor and turbo pinch-off regions. In the former one, a tight union bound and design criterion on the asymptotic performance are first derived, which provide an useful tool to predict the error performance. Based on the design criterion, an optimal rate-1 code for each 1-D mapping is then constructed to achieve the best asymptotic performance. In the turbo pinch-off area, by using extrinsic information transfer (EXIT) chart, the most suitable mixed codes are selected for both symmetric and asymmetric antenna setups. It is demonstrated that the simple concatenation scheme can achieve near-capacity. Furthermore, its error performance is comparable to that obtained by using well-designed irregular low-density parity-check (LDPC) and repeat accumulate (RA) codes, and thereby, outperforms a scheme employing a parallel concatenated turbo code.

关键词： Parity check codes Convolutional codes Block codes Phase shift keying Quadrature phase shift keying Transmitting antennas Error analysis Performance analysis Concatenated codes Turbo codes

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