waashk/dblp huggingface dataset:
exploitation of srtm dem in insar processing and its application to phase unwrapping problem. a novel approach is proposed in this paper to exploit the shuttle radar topography mission (srtm) digital elevation model (dem) in the interferometric synthetic aperture radar interferometry (insar) processing. the proposed algorithm includes three steps: the first step is to patch the void cells in the srtm dem; the second step is to determine a one-to-one correspondence between the interferogram and the srtm dem; the third step is to eliminate the phase trend between the original and simulated interferogram. meanwhile this algorithm can be applied to help the phase unwrapping problem. conventional techniques approach phase unwrapping as an optimization problem, where the total branch-cuts, or the gradient errors, etc. are to be minimized. generally speaking, they consider phase unwrapping as a blind procedure, i.e., without any external guidance. the purpose of this paper is to fill this gap by introducing the srtm dem as a phase unwrapping guidance. some experimental results with jesr verify our theoretical analysis and show that our method can improve the performance of the phase unwrapping to a great degree.
nonparametric estimation for compound poisson processes on compact lie groups. motivated by applications in multiple scattering, we study the problem of decompounding on compact lie groups. employing tools from harmonic analysis, we give a nonparametric approach to this problem. the case of the special orthogonal group so(3) is discussed in detail.
a reduced-rank square root filtering framework for noninvasive functional imaging of volumetric cardiac electrical activity. to noninvasively reconstruct transmembrane potential (tmp) dynamics throughout the 3d myocardium using body surface potential recordings, it is necessary to combine prior physiological models and patient's data with regard to their respective uncertainties. to fulfill model-data melding for this large-scale and high-dimensional system, data assimilation with proper computational reduction is needed for computational feasibility and efficiency. in this paper, we develop a reduced-rank square root tmp estimation algorithm, using dominant components of estimation uncertainties to guide a more efficient model-data coupling in the square root structure. the svd-based reduced-rank error covariance is used to represent and track the dominant estimation errors, and unified into an integrated square root filtering framework. phantom experiments demonstrate the ability of this framework to bring substantial computational reduction at slight expense of degraded estimation accuracy. it therefore improves the efficiency and applicability of the volumetric myocardial tmp imaging in practice.
voice conversion for various types of body transmitted speech. in this paper, we review our proposed statistical voice conversion approaches to enhancing various types of body transmitted speech captured with non-audible murmur (nam) microphone. body transmitted speech conversion is a potential technique to bring a new paradigm to human-to-human speech communication. in addition to our previously proposed methods of enhancing body transmitted unvoiced speech for silent speech communication and of enhancing body transmitted artificial speech for speaking aid, we further propose conversion methods of enhancing body transmitted voiced speech for noise robust speech communication. an experimental result demonstrates that the proposed methods yield significant improvements in quality of body transmitted voiced speech.
spatial structure characterization of textures in ihls colour space. we present model based approaches for colour texture characterization in ihls colour space. pure chrominance structure information is used in parallel with luminance structure information for colour texture classification. hue and saturation channels are combined through a complex exponential to give a single channel which holds all the chrominance information of the image. two dimensional complex multichannel versions of non-symmetric half plane autoregressive model and gauss markov random field model are used to perform parametric power spectrum estimation of both luminance and the “combined chrominance” channels of the image. colour texture classification is done using k-nearest neighbor algorithm on spectral distance measures both for luminance and chrominance channels individually as well as combined through a combination coefficient. experimental results show that colour texture characterization obtained by combined luminance and chrominance structure informations is better than the one obtained by using only luminance structure information.
improved sift-based image registration using belief propagation. scale invariant feature transform (sift) is a very powerful technique for image registration. while sift descriptors accurately extract invariant image characteristics around keypoints, the commonly used matching approach for registration is overly simplified, because it completely ignores the geometric information among descriptors. in this paper, we formulate keypoint matching as a global optimization problem and provide a suboptimum solution using belief propagation. experimental results show significant improvement over previous approaches.
a suffix array approach to video copy detection in video sharing social networks. to address the multiplicity and copyright issues on file sharing social networks, we propose a fast video copy detection algorithm using the suffix array data structure in this work. the proposed algorithm consists of two steps. in the first step, we extract robust features which are discriminative yet insensitive to various attacks. specifically, we develop a compact one-dimensional signature based on the shot change position of video files. unlike images and audio, the size of a video file is usually large, which makes it computationally expensive to match two long signature sequences. thus, in the second step, we adopt an efficient matching technique based on the suffix array data structure. the proposed system can perform the sequence matching in linear time while the complexity of conventional duplicate video detection algorithms grows at least quadratically with the video length.
semi-blind channel estimation for mimo single carrier with frequency domain equalization systems. we propose a semi-blind channel estimation method for multiple-input multiple-output (mimo) single carrier with frequency domain equalization systems. by taking advantage of periodic precoding and the block circulant channel model after cyclic prefix removal, we obtain the channel product matrices by solving a series of decoupled linear systems, which is gained from the covariance matrix of the received data. then the channel impulse response matrix is obtained by computing the positive eigenvalues and eigenvectors of a hermitian matrix formed from the channel product matrices. we also propose an optimal design of the precoding sequence which minimizes the noise effect and numerical error in covariance matrix estimation. simulations are used to demonstrate the performance of the proposed method. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
optimization of training sequences for spatially correlated mimo-ofdm. the optimal training sequence for channel estimation in spatially correlated multiple-input multiple-output (mimo) orthogonal frequency-division multiplexing (ofdm) systems has not been found for an arbitrary signal-to-noise ratio (snr). only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high snr. provided in this paper are (i) a necessary and sufficient condition for the optimal training sequence together with a convex programming to find the solution, and (ii) efficient procedures to find the optimal training sequence. simulation results confirms the superiority of the proposed design over the existing one.
a beamforming particle filter for eeg dipole source localization. recently we have developed a method for electroencephalogram (eeg) dipole source localization based on particle filtering (pf). in this study the method is combined with beamforming to eliminate the noise which is spatially uncorrelated with the desired signal and accordingly to improve its performance. the proposed beamforming is an optimum, linear and data independent filter which can be applied to stationary as well as non-stationary data. simulation and real data results have been provided to show its better performance over pf and beamforming approaches for dipole source localization.
rhythm map: extraction of unit rhythmic patterns and analysis of rhythmic structure from music acoustic signals. this paper discusses an approach to extract constituent percussive bar-long patterns in a music piece given as acoustic signal and to analyze the music structure with a map of constituent rhythmic patterns. possible applications include music genre classification, music information retrieval (mir) and music modification such as replacing rhythmic patterns with others. we propose a mathematical method based on one-pass dp algorithm and k-means clustering to extract unit percussive rhythmic patterns. as the result of identifying and localization the unit patterns in the entire piece, we obtained a music structure in the form of a map of rhythmic patterns.
cheat-proof cooperation strategies for wireless live streaming social networks. multimedia social network analysis is an emerging research area, which analyzes the behavior of users who share multimedia content and investigates the impact of human dynamics on multimedia systems. users watching live streaming in the same wireless network share the same backbone connection to the internet, thus they might want to cooperate with each other to obtain better video quality. these users form a wireless live-streaming social network and every user wishes to watch video with as high as possible quality while paying as less as cost for cooperation. thus full cooperation cannot be guaranteed and the cooperation strategy must give incentives to the users. this paper proposes a game-theoretic framework to model user behavior and designs incentive-based strategies to stimulate user cooperation in wireless live streaming. we analyze the pareto optimality and time-restricted bargaining equilibrium of the game. we also take into consideration selfish users' cheating behavior and propose cheat-proof strategies. both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free and help provide reliable services.
throughput analysis of cooperative wireless medium access scheme exploiting multi-beam adaptive arrays. cooperative wireless network medium access schemes can achieve high throughput through collision resolution. by using a multi-beam adaptive array (mbaa) at a base station or access point, it can concurrently communicate with multiple nodes/users and thus the network performance can be further enhanced. in this paper, we provide an efficient packet resolution method and analyze the throughput of cooperative wireless medium access scheme exploiting mbaas.
speaker adaptation by variable reference model subspace and application to large vocabulary speech recognition. recently, we presented a rapid speaker adaptation technique, reference model interpolation (rmi), which is based on the linear interpolation of speaker-dependent models and the a posteriori selection of reference models. the approach uses the a priori knowledge provided by a set of representative speakers to guide the estimation of a new speaker model in the speaker space. rmi achieved rapid supervised adaptation in phoneme decoding tasks. in this paper, we present two new results of rmi: firstly, we apply the rmi technique in a practical large vocabulary continuous speech recognition (lvcsr) system with unsupervised instantaneous adaptation. secondly, we propose an evolutional subspace scenario which integrates the slow update of reference models with rmi rapid adaptation to achieve incremental adaptation. the unsupervised adaptation experiments carried out on broadcast news transcription task show encouraging results for both instantaneous and incremental adapatation.
non-speech audio event detection. audio event detection is one of the tasks of the european project vidivideo. this paper focuses on the detection of non-speech events, and as such only searches for events in audio segments that have been previously classified as non-speech. preliminary experiments with a small corpus of sound effects have shown the potential of this type of corpus for training purposes. this paper describes our experiments with svm and hmm-based classifiers, using a 290-hour corpus of sound effects. although we have only built detectors for 15 semantic concepts so far, the method seems easily portable to other concepts. the paper reports experiments with multiple features, different kernels and several analysis windows. preliminary experiments on documentaries and films yielded promising results, despite the difficulties posed by the mixtures of audio events that characterize real sounds.
generalized baum-welch algorithm for discriminative training on large vocabulary continuous speech recognition system. we propose a new optimization algorithm called generalized baum welch (gbw) algorithm for discriminative training on hidden markov model (hmm). gbw is based on lagrange relaxation on a transformed optimization problem. we show that both baum-welch (bw) algorithm for ml estimate ofhmmparameters, and the popular extended baum-welch (ebw) algorithm for discriminative training are special cases of gbw.we compare the performance of gbw and ebw for farsi large vocabulary continuous speech recognition (lvcsr).
benchmarking flexible adaptive time-frequency transforms for underdetermined audio source separation. we have implemented several fast and flexible adaptive lapped orthogonal transform (lot) schemes for underdetermined audio source separation. this is generally addressed by time-frequency masking, requiring the sources to be disjoint in the time-frequency domain. we have already shown that disjointness can be increased via adaptive dyadic lots. by taking inspiration from the windowing schemes used in many audio coding frameworks, we improve on earlier results in two ways. firstly, we consider non-dyadic lots which match the time-varying signal structures better. secondly, we allow for a greater range of overlapping window profiles to decrease window boundary artifacts. this new scheme is benchmarked through oracle evaluations, and is shown to decrease computation time by over an order of magnitude compared to using very general schemes, whilst maintaining high separation performance and flexible signal adaptivity. as the results demonstrate, this work may find practical applications in high fidelity audio source separation.
orthogonalized discriminant analysis based on generalized singular value decomposition. generalized singular value decomposition (gsvd) has been used for linear discriminant analysis (lda) to solve the small sample size problem in pattern recognition. however, this algorithm may suffer from the over-fitting problem. in this paper, we propose a novel orthogonalization technique for the lda/gsvd algorithm to address the over-fitting problem. in this technique, an orthogonalization of the basis of the discriminant subspace derived from the lda/gsvd algorithm is carried out through an eigen-decomposition of a small size inner product matrix. it is computationally efficient when data are high dimensional. the technique is further applied to the kernelized lda/gsvd algorithm, mgsvd-kda, leading to a new algorithm, referred to as gsvd-okda. it is shown that with linear and nonlinear kernels, this new algorithm successfully overcomes the over-fitting problem of the lda/gsvd and mgsvd-kda algorithms. simulation results show that the proposed algorithms provide high recognition accuracy with low computational complexity.
identifiability and performance concerns for location estimation. common localization approaches require a large amount of information to be available in order to achieve identifiability, when the signal propagates in a strictly non-line-of-sight (nlos) environment. furthermore, even if they achieve identifiability, they usually perform poorly. in this contribution we investigate the conditions that must be met for identifiability to be feasible and for performance to be adequate. through basic theorems and simple numerical examples, we study the benefit of exploiting additional information, if such is available and we provide intuitive conclusions that can be proved useful for any localization scheme.
bandwidth-efficient cache-based motion compensation architecture with dram-friendly data access control. for h.264/avc decoder system, the motion compensation bandwidth comes from two parts, the reference data loading bandwidth and the equivalent bandwidth from dram access overhead latency. in this paper, a bandwidth-efficient cache-based mc architecture is proposed. it exploits both intra-mb and inter-mb data reuse and reduce up to 46% mc bandwidth compared to conventional scheme. to reduce the equivalent bandwidth from dram access overhead latency, the dram-friendly data mapping and access control scheme are proposed. they can reduce averagely 89.8% of equivalent dram access overhead bandwidth. the average mc burst length can be improved to 9.59 words/burst. the total bandwidth reduction can be up to 32∼71% compared to previous works.
discriminative pronounciation learning using phonetic decoder and minimum-classification-error criterion. in this paper, we report our recent research aimed at improving the pronunciation-modeling component of a speech recognition system designed for mobile voice search. our new discriminative learning technique overcomes the limitation of the traditional ways of introducing alternative pronunciations that often enlarge confusability across different lexical items. instead, we make use of a phonetic recognizer to generate pronunciation candidates, which are then evaluated and selected using the global minimum-classification-error measure, guaranteeing a reduction of the training-set error rate after introducing alternative pronunciations. a maximum entropy approach is subsequently used to learn the weight parameters of the selected pronunciation candidates. our experimental results demonstrate the effectiveness of the discriminative pronunciation learning technique in a real-world speech recognition task where pronunciation of business names presents special difficulty for high-accuracy speech recognition.
improved virtual channel noise model for transform domain wyner-ziv video coding. distributed video coding (dvc) has been proposed as a new video coding paradigm to deal with lossy source coding using side information to exploit the statistics at the decoder to reduce computational demands at the encoder. a virtual channel noise model is utilized at the decoder to estimate the noise distribution between the side information frame and the original frame. this is one of the most important aspects influencing the coding performance of dvc. noise models with different granularity have been proposed. in this paper, an improved noise model for transform domain wyner-ziv video coding is proposed, which utilizes cross-band correlation to estimate the laplacian parameters more accurately. experimental results show that the proposed noise model can improve the rate-distortion (rd) performance.
extended vts for noise-robust speech recognition. model compensation is a standard way of improving speech recognisers' robustness to noise. currently popular schemes are based on vector taylor series (vts) compensation. they often use the continuous time approximation to compensate dynamic parameters. in this paper, the accuracy of dynamic parameter compensation is improved by representing the dynamic features as a linear transformation of a window of static features. a modified version of vts compensation is applied to the distribution of the window of static features and, importantly, their correlations. these compensated distributions are then transformed to standard static and dynamic distributions. the proposed scheme outperformed the standard vts scheme by about 10% relative.
a fast and accurate "shoebox" room acoustics simulator. we present a new “shoebox” room acoustics simulator that is designed to support research into signal processing algorithms that are robust to reverberation. it is an improvement over existing room acoustics simulators because it is computationally fast, portable to many kinds of research environments, and flexible to use. the proposed simulator is also perceptually accurate because it models both specular and diffuse surface reflections. an efficient implementation of the simulator is made freely available for download from the open source project roomsim on sourceforge.
a dimensional approach to emotion recognition of speech from movies. in this paper we present a novel method for extracting affective information from movies, based on speech data. the method is based on a 2-d representation of speech emotions (emotion wheel). the goal is twofold. first, to investigate whether the emotion wheel offers a good representation for emotions associated with speech signals. to this end, several humans have manually annotated speech data from movies using the emotion wheel and the level of disagreement has been computed as a measure of representation quality. the results indicate that the emotion wheel is a good representation of emotions in speech data. second, a regression approach is adopted, in order to predict the location of an unknown speech segment in the emotion wheel. each speech segment is represented by a vector of ten audio features. the results indicate that the resulting architecture can estimate emotion states of speech from movies, with sufficient accuracy.
formant-based technique for automatic filled-pause detection in spontaneous spoken english. detection of filled pauses is a challenging research problem which has several practical applications. it can be used to evaluate the spoken fluency skills of the speaker, to improve the performance of automatic speech recognition systems or to predict the mental state of the speaker. this paper presents an algorithm for filled pause detection that is based on the premise that the vocal tract characteristics, and hence the formants, are stable during the production of a filled pause. the performance of the proposed algorithm is evaluated on real-life recordings of call center agents where the locations of the filled pauses are hand labeled. the proposed algorithm outperforms a standard cepstral stability based filled pause detection algorithm and a standard pitch-based detection technique.
higher dimensional consensus algorithms in sensor networks. this paper introduces higher dimensional consensus, a framework to capture a number of different, but, related distributed, iterative, linear algorithms of interest in sensor networks. we show that, by suitably choosing the iteration matrix of the higher dimensional consensus, we can capture, besides the standard average-consensus, a broad range of applications, including sensor localization, leader-follower, and distributed jacobi algorithm. we work with the concept of anchors and explicitly derive the consensus subspace and provide the dimension of the limiting state of the sensors.