waashk/dblp huggingface dataset:
assessing robustness of particle filtering by the kolmogorov-smirnov statistics. one of the most criticized aspects of particle filtering algorithms is their dependence on model assumptions. however, a rigorous study of the effect of modeling errors on the performance of such algorithms is still missing. in this paper, the problem of using an inaccurate discrete state-space model is considered and a systematic methodology for studying the effects on its performance is proposed. the methodology is based on the use of the kolmogorov-smirnov statistic, which in this case is a distance metric between the posterior characterization when respectively correct and incorrect model assumptions are made. an example with functional and distributional inaccuracies is studied.
modal expansion of hrtfs: continuous representation in frequency-range-angle. this paper proposes a continuous hrtf representation in both 3d spatial and frequency domains. the method is based on the acoustic reciprocity principle and a modal expansion of the wave equation solution to represent the hrtf variations with different variables in separate basis functions. the derived spatial basis modes can achieve hrtf near-field and far-field representation in one formulation. the hrtf frequency components are expanded using fourier spherical bessel series for compact representation. the proposed model can be used to reconstruct hrtfs at any arbitrary position in space and at any frequency point from a finite number of measurements. analytical simulated and measured hrtfs from a kemar are used to validate the model.
a collaborative bayesian image retrieval framework. in this paper, an image retrieval framework combining content-based and content-free methods is proposed, which employs both short-term relevance feedback (strf) and long-term relevance feedback (ltrf) as the means of user interaction. the strf refers to iterative query-specific model learning during a retrieval session, and the ltrf is the estimation of a user history model from the past retrieval results approved by previous users. the framework is formulated based on the bayes' theorem, in which the results from strf and ltrf play the roles of refining the likelihood and the a priori information, respectively, and the images are ranked according to the a posteriori probability. since the estimation of the user history model is based on the principle of collaborative filtering, the system is referred to as a collaborative bayesian image retrieval (clbir) framework. to evaluate the effectiveness of the proposed framework, nearest neighbor clbir (nn-clbir) and support vector machine active learning clbir (svmal-clbir) were implemented. experimental results showed the improvement over content-based methods in terms of both accuracy and ranking due to the integration in the proposed framework.
transmit/receive beamforming for mimo radar with colocated antennas. we propose a new technique for multiple-input multiple-output (mimo) radar with colocated antennas. the essence of the proposed technique is to partition the transmitting array into a number of subarrays that are allowed to overlap. each subarray is used to coherently transmit a waveform which is orthogonal to the waveforms transmitted by other subarrays. coherent processing gain can be achieved by designing a weight vector for each subarray to form a beam towards a certain direction in space. moreover, the subarrays are combined jointly to form a mimo radar resulting in higher resolution capabilities. simulation results show the substantial improvements offered by the proposed technique as compared to previous techniques that validate its effectiveness.
doa estimation method based on sparseness of speech sources for human symbiotic robots. in this paper, direction of arrival (doa) estimation methods (both azimuth and elevation) based on sparseness of human speech, “modified delay-and-sum beamformer based on sparseness (mdsbf)” and “stepwise phase difference restoration (spire)”, are introduced for human symbiotic robots. mdsbf can achieve good doa estimation, whose computational cost is proportional to resolution of azimuth and elevation space. doa estimation result of spire is less accurate than that of mdsbf, but computational cost is independent of resolution. to achieve more accurate doa estimation result than spire with small computational cost, we propose a novel doa estimation method which is combination of mdsbf and spire. in the proposed method, mdsbf with rough resolution is performed prior to spire execution, and spire precisely estimates doa of sources after mdsbf. experimental results show that sparseness based methods are superior to conventional methods. the proposed combination method achieved more accurate doa estimation result than spire with smaller computational cost than mdsbf.
design of a morphological moving object signature and application to human identification. many computer vision systems try to infer semantic information about a video scene content by looking at the time series of the silhouettes of the moving objects. this paper proposes a new inter-frame feature set (signature) based on piecewise surfacic descriptions of binary silhouettes. it captures the dynamics of moving objects and compacts it into a robust set of features suitable for classification. to assess its ability to represent motion information, we use it to build a complete gait recognition algorithm that we test on a database of 21 different subjects. to highlight the efficiency of our signature, we use frontal views instead of side views of persons, which is less discussed in literature and is considered to be harder as the movement of legs is not visible. in that context, the high recognition rates obtained (over 95% of correct identifications) proves that our signature is appropriate to describe moving objects.
flatbed scanner identification based on dust and scratches over scanner platen. in this paper, a novel individual source scanner identification scheme is proposed. the scheme uses traces of dust, dirt, and scratches over scanner platen on scanned images to characterize a source scanner. the efficacy of the proposed scheme is substantiated with experimental analysis. the robustness of the scheme to the jpeg compression is also investigated. experimental results show that proposed scheme could be used to match a scanned image to its source.
a new method for tracking performance evaluation based on a reflective model and perturbation analysis. in this paper, we present a novel methodology for tracking performance evaluation. considering the continuity of the image sequences in a video, we define a new measurement called tracking difficulty which incorporates the local sequence information among a small image sequence centered at each frame. we subsequently use a reflective model to formulate tracking difficulty. tracking difficulty curves can not only illustrate at which parts of the video one tracking algorithm performs well or poor, but also provide a way to compare the performance of different tracking algorithms. we further add perturbation analysis to the reflective model to examine how sensitive the tracking algorithm is to noise. results on data sets are presented to show the effectiveness of our evaluation method.
a differential geometric approach to discrete-coefficient filter design. this paper is concerned with the problem of computing a discrete-coefficient approximation to a digital filter. in contrast to earlier works that have approached this problem using standard combinatorial optimization tools, we take a geometric approach. we define a riemannian manifold, arising from the difference in frequency response between the two systems of interest, on which we design efficient algorithms for sampling and approximation. this additional structure enables us to tame the computational complexity of the native combinatorial optimization problem. we illustrate the benefits of this approach with design examples involving iir and fir filters.
an efficient first-order method for l1 compression of images. we consider the problem of lossy compression of images using sparse representations from overcomplete dictionaries. this problem is in principle easy to solve using standard algorithms for convex programming, but often the large dimensions render such an approach intractable. we present a highly efficient method based on recently developed first-order methods, which enables us to compute sparse approximations of entire images with modest time and memory consumption.
periodically gapped data spectral velocity estimation in medical ultrasound using spatial and temporal dimensions. modern medical ultrasound scanners estimate blood velocity distribution by computing the spectrogram of a temporal data sequence, typically using periodogram methods which require long observation windows. furthermore, an additional b-mode image is often displayed, resulting in gaps in the data at b-mode emissions. we propose a data-adaptive velocity estimator for periodically gapped (pg) data that extends pg-capon and pg-apes by using two dimensional spatial and temporal data to estimate a one dimensional spectrum. we show through realistic flow simulations that our method improves spectral resolution and reduces leakage in comparison to pg-capon, pg-apes, and correlogram based gapped data velocity estimators, potentially increasing the maximum detectable velocity and temporal resolution of blood flow using ultrasound.
voice conversion using artificial neural networks. in this paper, we propose to use artificial neural networks (ann) for voice conversion. we have exploited the mapping abilities of ann to perform mapping of spectral features of a source speaker to that of a target speaker. a comparative study of voice conversion using ann and the state-of-the-art gaussian mixture model (gmm) is conducted. the results of voice conversion evaluated using subjective and objective measures confirm that anns perform better transformation than gmms and the quality of the transformed speech is intelligible and has the characteristics of the target speaker.
optimal gaussian fingeprint decoders. this paper proposes codes that achieve the fundamental capacity limits of digital fingerprinting subject to mean-squared distortion constraints on the fingerprint embedder and the colluders. we first show that the traditional method of fingerprint decoding by thresholding correlation statistics falls short of this goal: reliable performance is impossible at code rates greater than some value c1 that is strictly less than capacity. to bridge the gap to capacity, a more powerful decoding method is needed. the maximum penalized gaussian mutual information decoder presented here meets this requirement. finally, a mathematical framework and a capacity expression for fingerprinting of social networks are presented.
emotional speech recognition based on style estimation and adaptation with multiple-regression hmm. this paper proposes a technique for emotional speech recognition which enables us to extract paralinguistic information as well as linguistic information contained in speech signal. the technique is based on style estimation and style adaptation using multiple-regression hmm. recognition process consists of two stages. in the first stage, a style vector that represents the emotional expression category and intensity of its variation of input speech is estimated on a sentence-by-sentence basis. then the acoustic models are adapted using the estimated style vector and standard hmm-based speech recognition is performed in the second stage. we assess the performance of the proposed technique on the recognition of acted emotional speech uttered by both professional narrators and non-professional speakers and show the effectiveness of the technique.
distributed sensing of signals linked by sparse filtering. we consider the task of recovering correlated vectors at a central decoder based on fixed linear measurements obtained by distributed sensors. a general formulation of the problem is proposed, under both a universal and an almost sure reconstruction requirement. we then study a specific correlation model which involves a filter that is sparse in the time domain. while this sparsity assumption does not allow reducing the description cost in the universal case, we show that large gains can be achieved in the almost sure scenario by means of a novel distributed scheme based on annihilating filters. the robustness of the proposed method is also investigated.
fft-based estimation of large motions in images: a robust gradient-based approach. a fast and robust gradient-based motion estimation technique which operates in the frequency domain is presented. the algorithm combines the natural advantages of a good feature selection offered by gradient-based methods with the robustness and speed provided by fft-based correlation schemes. experimentation with real images taken from a popular database showed that, unlike any other fourier-based techniques, the method was able to estimate translations, arbitrary rotations and scale factors in the range 4–6.
exploiting user feedback for language model adaptation in meeting recognition. we investigate languagemodel (lm) adaptation in a meeting recognition application, where the lm is adapted based on recognition output from relevant prior meetings and partial manual corrections. unlike previous work, which has considered either completely unsupervised or supervised adaptation, we investigate a scenario where a human (e.g., a meeting participant) can correct some of the recognition mistakes. we find that recognition accuracy using the adapted lm can be enhanced substantially by partial correction. in particular, if all content words (about half of all recognition errors) are corrected, recognition improves to the same accuracy as if completely error-free (manually created) transcriptions had been used for adaptation. we also compare and combine a variety of adaptation methods, including linear interpolation, unigram marginal adaptation, and a discriminative method based on “positive” and “negative” n-grams.
ifly system for the nist 2008 speaker recognition evaluation. the description of ifly system submitted for nist 2008 speaker recognition evaluation (sre), which has achieved excellent performance in the 2008 sre evaluation, is presented in this paper. our primary system is a fusion of two subsystems gmm-ubm and gmm-svm. for each sub-system, two kinds of short-time acoustic features plp and lpcc are adopted. we focus on three key issues in this evaluation: channel compensation, multi-lingual or bi-lingual cues and the voice activity detection. we also point out that data selection and factor analysis play key roles in the system improvement.
efficient subword lattice retrieval for german spoken term detection. we present a lattice-based std method for german broadcast news data and compare it to a previously proposed fuzzy search. due to the important out-of-vocabulary (oov) problem in german, we evaluate suitable subword indexing units for lattice retrieval. hybrid lattice retrieval of words and subwords is investigated because of the robust nature of words as an indexing unit. we show that by using efficient lattice graph and score pruning techniques, precision of subword retrieval is increased by 8% absolute with only a small loss in recall. additionally, a speed-up of up to 6 times can be observed.
higher order teager-kaiser operators for image analysis: part i - a monocomponent image demodulation. we present in this paper a new narrowband image demodulation method. our approach is based on the 2d higher order teager-kaiser operators (hotko). we show that the introduction of higher orders in the teager-kaiser operator, improves a lot the demodulation results, in comparison to the discrete energy separation algorithm (desa) and the analytic image (ai) method. more precisely, for synthetic images, we show that the approximation errors on both the amplitude and the frequency components are much more lower with our proposed demodulation method than the desa and the ai method. moreover, it turns out that for the presented real images, the algorithm is so efficient, especially the amplitude counterpart, that it tracks the most important parts in images, and segments the regions of interest. we show how the algorithm could be used in sonar images for extracting mines'shadows, which is very important for both military and civil applications.
connectivity similarity based transductive learning for interactive image segmentation. we propose a novel graph-based transductive learning approach for interactive image segmentation. here the term “transductive” indicates a process that iteratively propagates information from user-labeled regions to unlabeled image pixels. for the application of interactive image segmentation, transductive approach has several advantages compared with traditional color probabilistic model based approach. however, previous transductive approaches for image segmentation usually utilize an 8-connected neighborhood system, which has low efficacy when transferring local information to remote pixels. the main contribution of this paper is to estimate pairwise pixel similarity based on a novel path-based metric (i.e. connectivity similarity), rather than local comparison with 8-connected neighbors. we further theoretically prove the computing complexity is on a polynomial order and provide convergence guarantee for the extra local smoothing operation that is introduced to further refine the initial results. especially, the proposed method shows promising performance in the multi-label case. various experiments are presented to illustrate its effectiveness.
space-time-range three dimensional adaptive processing. space-time adaptive processing (stap) is an effective tool for moving target detection. conventional stap methodologies process the angular and doppler two dimensional data vector. in practical applications, adjacent range cells are statistically dependent due to filtering, since the point spreading function of a target is not an ideal delta function. in this paper, a novel approach incorporating range (fast time) information in stap is presented for clutter rejection, which we term space-time-range adaptive processing (strap). this method takes advantage of the correlation information of neighboring range cells. therefore, the stationary clutter can be suppressed better compared with traditional stap algorithms ignoring fast time information, resulting in more effective moving target detection. the validity of the strap algorithm is verified by the experiments of processing the real measured data of the three-channel x-band radar and mcarm radar systems.
robust modeling of musical chord sequences using probabilistic n-grams. the modeling of music as a language is a core issue for a wide range of applications such as polyphonic music retrieval, automatic style identification, audio to symbolic music transcription and computer-assisted composition. in this paper, we focus on the modeling of chord sequences by probabilistic n-grams. previous studies using these models have achieved limited success, due to overfitting and to the use of a single chord labeling scheme. we investigate these issues using model smoothing and selection techniques initially designed for spoken language modeling. this approach is evaluated over a set of songs by the beatles, considering several chord labeling schemes. initial results show that the accuracy of n-grams is increased but that additional improvements may still be achieved in the future using more advanced, possibly music-specific, smoothing techniques.
language model parameter estimation using user transcriptions. in limited data domains, many effective language modeling techniques construct models with parameters to be estimated on an in-domain development set. however, in some domains, no such data exist beyond the unlabeled test corpus. in this work, we explore the iterative use of the recognition hypotheses for unsupervised parameter estimation. we also evaluate the effectiveness of supervised adaptation using varying amounts of user-provided transcripts of utterances selected via multiple strategies. while unsupervised adaptation obtains 80% of the potential error reductions, it is outperformed by using only 300 words of user transcription. by transcribing the lowest confidence utterances first, we further obtain an effective word error rate reduction of 0.6%.
voice transformation: a survey. voice transformation refers to the various modifications one may apply to the sound produced by a person, speaking or singing. voice transformation is usually seen as an add-on or an external system in speech synthesis systems since it may create virtual voices in a simple and flexible way. in this paper we review the state-of-the-art voice transformation methodology showing its limitations in producing good speech quality and its current challenges. addressing quality issues of current voice transformation algorithms in conjunction with properties of the speech production and speech perception systems we try to pave the way for more natural voice transformation algorithms in the future. facing the challenges, will allow voice transformation systems to be applied in important and versatile areas of speech technology; applications that are far beyond speech synthesis. 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
action recognition in unconstrained amateur videos. in this paper, we propose a systematic framework for action recognition in unconstrained amateur videos. inspired by the success of local features used in object and pose recognition, we extract local static features from the sampled frames to capture local pose shape and appearance. in addition, we extract spatiotemporal features (st features), which have been successfully used in action recognition, to capture the local motions. in the action recognition phase, we use the pyramid match kernel based on weighted similarities of multi-resolution histograms to match two videos within the same feature types. in order to handle complementary but heterogeneous features, i.e., static and motion features, we chose a multi-kernel classifier for feature fusion. to reduce the noise introduced by the background clutter, our system also tries to automatically find the rough region of interest/action. preliminary tests on the kth action dataset, ucf sports dataset, and a youtube action dataset have shown promising results.
frame-level temporal calibration of unsynchronized cameras by using longest consecutive common subsequence. we present a computationally efficient and robust method for temporally calibrating video sequences from unsynchronized cameras by using object trajectories. existing methods remain restricted in terms of their assumptions, and/or they are computationally expensive. to match and align the object trajectories, and thus to recover the frame offset between video sequences, we present an algorithm that is based on the longest consecutive common subsequence. the candidate frame offsets are obtained from each matched trajectory pair, and then a confidence check is performed. the algorithm is robust against possible errors due to background subtraction and location extraction, and can handle large frame offsets. we present experimental results for different frame offset values on different video sequences, which show the robustness of the algorithm in recovering the frame offsets. we also compare the presented algorithm with our previous work to demonstrate the computational efficiency provided.