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17th Annual conference of the International-Speech-Communication-Association (INTERSPEECH 2016)

作者： Kinnunen, Tomi Sahidullah, Md Kukanov, Ivan Delgado, Hector Todisco, Massimiliano Sarkar, Achintya Thomsen, Nicolai Baek Hautamaki, Ville Evans, Nicholas Tana, Zheng-Hua Univ Eastern Finland Speech & Image Proc Unit Sch Comp Kuopio Finland EURECOM Digital Secur Biot France Aalborg Univ Dept Elect Syst Signal & Informat Proc Aalborg Denmark

ISBN: (纸本)9781510833135

Text-dependent automatic speaker verification naturally calls for the simultaneous verification of speaker identity and spoken content. These two tasks can be achieved with automatic speaker verification (ASV) and utterance verification (UV) technologies. While both have been addressed previously in the literature, a treatment of simultaneous speaker and utterance verification with a modern, standard database is so far lacking. This is despite the burgeoning demand for voice biometrics in a plethora of practical security applications. With the goal of improving overall verification performance, this paper reports different strategies for simultaneous ASV and UV in the context of short-duration, text-dependent speaker verification. Experiments performed on the recently released RedDots corpus are reported for three different ASV systems and four different UV systems. Results show that the combination of utterance verification with automatic speaker verification is (almost) universally beneficial with significant performance improvements being observed.

关键词： Speaker recognition Text-dependent Utterance verification Confidence measure

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Discrete Fourier and wavelet transforms :an introduction through linear algebra with applications to signal processing /

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2016年

作者： Goodman Roe W.Roe W. Goodman Rutgers University USA.

ISBN: (纸本)9789814725774

This textbook for undergraduate mathematics, science, and engineering students introduces the theory and applications of discrete Fourier and wavelet transforms using elementary linear algebra, without assuming prior knowledge of signal processing or advanced analysis. It explains how to use the Fourier matrix to extract frequency information from a digital signal and how to use circulant matrices to emphasize selected frequency ranges. It introduces discrete wavelet transforms for digital signals through the lifting method and illustrates through examples and computer explorations how these transforms are used in signal and image processing. Then the general theory of discrete wavelet transforms is developed via the matrix algebra of two-channel filter banks. Finally, wavelet transforms for analog signals are constructed based on filter bank results already presented, and the mathematical framework of multiresolution analysis is examined.

关键词： Fourier transformations.Algebras Linear.signal processing.wavelets (Mathematics)

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An Expectation Maximization approach to Joint Modeling of Multidimensional Ratings derived from Multiple Annotators 17

An Expectation Maximization approach to Joint Modeling of Mu...

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17th Annual conference of the International-Speech-Communication-Association (INTERSPEECH 2016)

作者： Ramakrishna, Anil Gupta, Rahul Grossman, Ruth B. Narayanan, Shrikanth S. Univ Southern Calif Signal Anal & Interpretat Lab Los Angeles CA 90007 USA Emerson Coll Dept Commun Sci & Disorders Boston MA 02116 USA

ISBN: (纸本)9781510833135

Ratings from multiple human annotators are often pooled in applications where the ground truth is hidden. Examples include annotating perceived emotions and assessing quality metrics for speech and image. These ratings are not restricted to a single dimension and can be multidimensional. In this paper, we propose an Expectation-Maximization based algorithm to model such ratings. Our model assumes that there exists a latent multidimensional ground truth that can be determined from the observation features and that the ratings provided by the annotators are noisy versions of the ground truth. We test our model on a study conducted on children with autism to predict a four dimensional rating of expressivity, naturalness, pronunciation goodness and engagement. Our goal in this application is to reliably predict the individual annotator ratings which can be used to address issues of cognitive load on the annotators as well as the rating cost. We initially train a baseline directly predicting annotator ratings from the features and compare it to our model under three different settings assuming: (i) each entry in the multidimensional rating is independent of others, (ii) a joint distribution among rating dimensions exists, (iii) a partial set of ratings to predict the remaining entries is available.

关键词： Multiple Annotator Modeling Expectation Maximization algorithm

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Higher-order graph wavelets and sparsity on circulant graphs 16

Higher-order graph wavelets and sparsity on circulant graphs

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conference on wavelets and Sparsity xVI

作者： Kotzagiannidis, Madeleine S. Dragotti, Pier Luigi Imperial Coll London Dept Elect & Elect Engn London England

ISBN: (纸本)9781628417630

The notion of a graph wavelet gives rise to more advanced processing of data on graphs due to its ability to operate in a localized manner, across newly arising data-dependency structures, with respect to the graph signal and underlying graph structure, thereby taking into consideration the inherent geometry of the data. In this work, we tackle the problem of creating graph wavelet filterbanks on circulant graphs for a sparse representation of certain classes of graph signals. The underlying graph can hereby be data-driven as well as fixed, for applications including image processing and social network theory, whereby clusters can be modelled as circulant graphs, respectively. We present a set of novel graph wavelet filterbank constructions, which annihilate higher-order polynomial graph signals (up to a border effect) defined on the vertices of undirected, circulant graphs, and are localised in the vertex domain. We give preliminary results on their performance for non-linear graph signal approximation and denoising. Furthermore, we provide extensions to our previously developed segmentationinspired graph wavelet framework for non-linear image approximation, by incorporating notions of smoothness and vanishing moments, which further improve performance compared to traditional methods.

关键词： signal processing on graphs graph wavelet circulant graph sparsity

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Wavelength-Modulated Differential Photoacoustic (WM-DPA) imaging: a high dynamic range modality towards noninvasive diagnosis of cancer

Wavelength-Modulated Differential Photoacoustic (WM-DPA) ima...

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conference on Photons Plus Ultrasound - Imaging and Sensing

作者： Dovlo, Edem Lashkari, Bahman Choi, Sung Soo Sean Mandelis, Andreas Univ Toronto Ctr Adv Diffus Wave Technol CADIFT Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada

ISBN: (纸本)9781628419429

This study explores wavelength-modulated differential photoacoustic (WM-DPA) imaging for non-invasive early cancer detection via sensitive characterization of functional information such as hemoglobin oxygenation (sO(2)) levels. Well-known benchmarks of tumor formation such as angiogenesis and hypoxia can be addressed this way. While most conventional photoacoustic imaging has almost entirely employed high-power pulsed lasers, frequency-domain photoacoustic radar (FD-PAR) has seen significant development as an alternative technique. It employs a continuous wave laser source intensity-modulated and driven by frequency-swept waveforms. WM-DPA imaging utilizes chirp modulated laser beams at two distinct wavelengths for which absorption differences between oxy- and deoxygenated hemoglobin are minimum (isosbestic point, 805 nm) and maximum (680 nm) to simultaneously generate two signals detected using a standard commercial array transducer as well as a single-element transducer that scans the sample. signal processing is performed using Lab View and Matlab software developed in-house. Minute changes in total hemoglobin concentration (tHb) and oxygenation levels are detectable using this method since background absorption is suppressed due to the out-of-phase modulation of the laser sources while the difference between the two signals is amplified, thus allowing pre-malignant tumors to become identifiable. By regulating the signal amplitude ratio and phase shift the system can be tuned to applications like cancer screening, 502 quantification and hypoxia monitoring in stroke patients. Experimental results presented demonstrate WM-DPA imaging of sheep blood phantoms in comparison to single-wavelength FD-PAR imaging. Future work includes the functional PA imaging of small animals in vivo.

关键词： Differential Photoacoustic imaging Wavelength modulation Functional imaging Frequency-domain Cross-correlation Biomedical imaging image improvement Premalignant

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Wavelet-Based Classification of Hyperspectral images Using Extended Morphological Profiles on Graphics processing Units

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2015年第6期8卷 2962-2970页

作者： Quesada-Barriuso, Pablo Argueello, Francisco Heras, Dora B. Benediktsson, Jon Atli Univ Santiago de Compostela Ctr Invest Tecnol Informac Santiago De Compostela 15842 Spain Univ Iceland Fac Elect & Comp Engn IS-107 Reykjavik Iceland

The availability of graphics processing units (GPUs) provides a low-cost solution to real-time processing, which may benefit many remote sensing applications. In this paper, a spectral-spatial classification scheme for hyperspectral images is specifically adapted for computing on GPUs. It is based on wavelets, extended morphological profiles (EMPs), and support vector machine (SVM). Additionally, a preprocessing stage is used to remove noise in the original hyperspectral image. The local computation of the techniques used in the proposed scheme makes them particularly suitable for parallel processing by blocks of threads in the GPU. Moreover, a block-asynchronous updating process is applied to the EMP to speedup the morphological reconstruction. The results over different hyperspectral images show that the execution can be speeded up to 8.2x compared to an efficient OpenMP parallel implementation, achieving real-time hyperspectral image classification while maintaining the high classification accuracy values of the original classification scheme.

关键词： Feature extraction graphics processing unit (GPU) image classification morphological operations parallel processing remote sensing wavelet transforms

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Improving accuracy of double reading in chest x-ray images by using eye-gaze metrics

Improving accuracy of double reading in chest X-ray images b...

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IEEE signal processing and Communications applications (SIU)

作者： Ulaş Vural H. Tahsin Sarısoy Gür Akansel Istanbul Medeniyet Universitesi Istanbul Istanbul TR Kocaeli Universitesi Kocaeli TR

Accurate diagnosis of issues using medical images is crucial. One common approach for decreasing the risk of human diagnosis error is the double reading which is based on the assessment of each medical image by more than one radiologist. A novel method which increases the accuracy of the double reading approach while decreasing the required number of expert radiologist is proposed. The core idea behind the method is the better selection of the second expert by using subjective difficulty level of the medical image for the first doctor. The method estimates the subjective difficulty level by using eye-gaze metrics and the method assigns difficult images to the more experienced radiologists. The expertise level of the doctors are determined by the number of their previous decisions which are matched with the other experts' decisions. Effectiveness of the proposed system is shown by using real chest x-ray images and eye-gaze recordings.

关键词： x-ray imaging Medical diagnostic imaging Measurement Tumors

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Sensor signals Monitoring and Control Using wavelets Transform Representation Algorithm 6

Sensor Signals Monitoring and Control Using Wavelets Transfo...

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6th International conference on Graphic and image processing (ICGIP)

作者： Paul, Okuwobi Idowu Lu, Yonghua Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 211106 Jiangsu Peoples R China

ISBN: (纸本)9781628415582

The usefulness of wavelet transforms has been compared and contrasted to Fourier transforms. Most importantly, wavelets transform provide a much needed alternative to Fourier transform for certain application such as pattern based monitoring and control. Effort has been made to provide a technique to extract essential trends from process signals and provide a compact representation. The effectiveness of a signal processing technique depends to a large extent on the nature of the signals involved. On technique that works for specific signal trends might not be effective in dealing with other signal trends. More so in pre-processing stage, signal extension has been identified as the critical factor influencing signal representation and retention of trends. This paper introduce a new algorithm in solving the present problems in sensor signal monitoring and control. The New Extension Technique (NET) was introduced, which provide an accurate wavelet decomposition irrespective of the nature of the signal. This method uses a statistical approach to provide a good approximation of the signal outside the boundaries of the signal depending on signal trends at the boundaries. Different statistical approaches were adopted for this purpose and four new extension methods were also introduced in order to ascertain which extension methods provide a reliable extension for all cases. The concept behind these methods is the same, since the signal samples close to the boundary are considered and a mean value is determined. The procedure for determining this mean value differs for each of these four methods;NET A, NET B, NET C, and NET D. The signal is then extended by making it symmetric with respect to that mean value and then inverting it.

关键词： wavelets Monitoring Extension Technique Transform

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In-network adaptive cluster enumeration for distributed classification and labeling

In-network adaptive cluster enumeration for distributed clas...

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European signal processing conference (EUSIPCO)

作者： Freweyni K. Teklehaymanot Michael Muma Jun Liu Abdelhak M. Zoubir Graduate School CE Technische Universität Darmstadt Darmstadt Germany Signal Processing Group Technische Universität Darmstadt Darmstadt Germany

A crucial first step for signal processing decentralized sensor networks with node-specific interests is to agree upon a common unique labeling of all observed sources in the network. The knowledge “who observes what” is required, e.g. in node-specific audio or video signal enhancement to form node clusters of common interest. Recently proposed in-network distributed adaptive classification and labeling algorithms assume knowledge on the number of objects (clusters), which is not necessarily available in real-world applications. Thus, we consider the problem of estimating the number of data-clusters in the distributed adaptive network set-up. We propose two distributed adaptive cluster enumeration methods. They combine the diffusion principle, where the nodes share information within their local neighborhood only (without fusion center), with the x-means and the PG-means cluster enumeration. Performance is evaluated via simulations and the applicability of the methods is illustrated using a distributed camera network where moving objects appear and disappear from the Line-of-Sight (LOS) and the number of clusters becomes time-varying.

关键词： Cameras Clustering algorithms signal processing Labeling signal processing algorithms Convergence image color analysis

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Spatio-Temporal Wavelet Transforms for image Sequence Analysis 8

Spatio-Temporal Wavelet Transforms for Image Sequence Analys...

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8th European signal processing conference, EUSIPCO 1996

作者： Leduc, J.P. Labit, C. Irisa /Centre Inria -Rennes Campus de Beaulieu Avenue du General Leclerc RennesF-35042 France

This paper intends to present an integrated approach of con-structing new spatio-temporal wavelets for discrete signal *** main illustrative field of applications considered here stands as the analysis of digital image ***-less,this can be readily extended to any kind of spatio-temporal *** wavelet transforms,continuous series,dis-cretized series and discrete transforms are considered here in anunified *** analysis to be developed relies only on dynamic parameters like uniform translation and rotation,on kinematic parameters like velocity and speed and on structural parameters as scale and *** digital processing intends to cover the detection and the focalization on motion-based regions of in-terest in order to perform tracking, classification, segmentation, multiscale trajectory construction and eventually a selective re- construction of the useful content. © 2015 European signal processing conference, EUSIPCO. All rights reserved.

关键词： image analysis

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