waashk/dblp huggingface dataset:
monotonic optimization framework for the miso ifc. resource allocation and transmit optimization for the multiple-antenna gaussian interference channel are important but difficult problems. recently, there has been a large interest in algorithms that find operating points which are optimal in the sum-rate, proportional-fair, or minimax sense. finding these points entails solving a nonlinear, non-convex optimization problem. in this paper, we develop an algorithm that solves these problems exactly, to within a prescribed level of accuracy and in a finite number of steps. the main idea is to rewrite the objective functions so that methods for monotonic optimization can be used. more precisely, we write each objective function as a difference between two functions which are strictly increasing over a normal constraint set. the so-obtained reformulated, equivalent problem can then be solved efficiently by using so-called polyblock optimization. numerical examples illustrate the advantages of the proposed framework compared to an exhaustive grid search.
a unified view for discriminative objective functions based on negative exponential of difference measure between strings. this paper presents a novel unified view of a wide variety of objective functions suitable for discriminative training applied to sequential pattern recognition problems, such as automatic speech recognition. focusing on a central component of conventional objective functions, the sum of modified joint probabilities of observations and strings, the analysis generalizes these objective functions by weighting each term in the sum by an important function, the negative exponential of difference measure between strings. the interesting and valuable results of this investigation are highlighted in a comprehensive relationship chart that covers all of the common approaches (maximum mutual information, minimum classification error, minimum phone/word error), as well as corresponding novel generalizations and modifications of those approaches.
using filter banks to enhance images for fluid lens cameras based on color correlation. the novel field of fluid lens cameras introduces unique image processing challenges. intended for surgical applications, these fluid optics systems have improved miniaturization over glass lenses and do not have moving parts while zooming. however, the liquid medium creates non-uniform color blur, which causes certain color planes to appear sharper than others. we propose an adapted perfect reconstruction filter bank that uses high frequency sub-bands of sharp color planes to improve blurred color planes. the approach is refined by adjusting the decomposition level based on limited channel information. this paper primarily considers the use of a sharp green color plane to improve a blurred blue color plane. more generally, these methods could improve the red color plane as well as any system with high edge correlation between two images.
maximum-likelihood estimation of autoregressive models with conditional independence constraints. we propose a convex optimization method for maximum likelihood estimation of autoregressive models, subject to conditional independence constraints. this problem is an extension to times series of the classical covariance selection problem in graphical modeling. the conditional independence constraints impose quadratic equalities on the autoregressive model parameters, which makes the maximum likelihood estimation problem nonconvex and difficult to solve. we formulate a convex relaxation and prove that it is exact when the sample covariance matrix is block-toeplitz. we also observe experimentally that in practice the relaxation is exact under much weaker conditions. we discuss applications to topology selection in graphical models of time series, by enumerating all possible topologies, and ranking them using information-theoretic model selection criteria. the method is illustrated by an example of air pollution data.
no-reference video quality evaluation for high-definition video. a no-reference video quality metric for high-definition video is introduced. this metric evaluates a set of simple features such as blocking or blurring, and combines those features into one parameter representing visual quality. while only comparably few base feature measurements are used, additional parameters are gained by evaluating changes for these measurements over time and using additional temporal pooling methods. to take into account the different characteristics of different video sequences, the gained quality value is corrected using a low quality version of the received video. the metric is verified using data from accurate subjective tests, and special care was taken to separate data used for calibration and verification. the proposed no-reference quality metric delivers a prediction accuracy of 0.86 when compared to subjective tests, and significantly outperforms psnr as a quality predictor.
using fourier-based shape alignment to add geometric prior to snakes. in this paper, we present a new algorithm of snakes with geometric prior. a method of shape alignment using fourier coefficients is introduced to estimate the euclidean transformation between the evolving snake and a template of the searched object. this allows the definition of a new field of forces making the evolving snake to have a shape similar to the template one. furthermore, this strategy can be used to manage several possible templates by computing a shape distance to select the best one at each iteration. the new method also solves some well-known limitations of snakes such as evolution in concave boundaries, and enhances the robustness to noise and partially occluded objects. a series of experimental results is presented to illustrate performances.
a semi-blind em algorithm for overcomplete ica. overcomplete independent component analysis (ica) is a challenge of ica to estimate more sources from less mixtures. the statistical properties of the sources such as sparsity are often assumed to solve the problem. other available information about the sources such as waveform, however, is scarcely used. motivated by the fact that semi-blind ica in complete case can improve the potential of ica by incorporating source information, this paper proposes a semi-blind algorithm for overcomplete ica by explicitly utilizing waveform information about some sources. an approximate expecta tion-maximization (em) algorithm is explored to provide normal cost function of the semi-blind algorithm while the prior information is utilized to form an extended one. computer simulations results demonstrate that the proposed algorithm has much improved performance in snr, convergence speed, and elimination of order ambiguity compared to the original em algorithm.
asynchronous stereo vision system for front-vehicle detection. this study uses two low-cost and compact cmos cameras to construct an asynchronous binocular system and proposes an effective real-time front-vehicle detection algorithm with the binocular system. the proposed vehicle detection algorithm uses the edge information to detect the region of each front vehicle, and then computes the disparities of the front vehicles by an adaptive search method. according to the disparity values, the relative distances between the front and the host vehicles can be computed. the proposed algorithm of the manuscript conquers the asynchronous exposure problem and cost issue of a binocular system. experimental results show that the proposed system can robustly and accurately detect obstacles or other vehicles in real time under different illumination and road conditions.
an improved approach for image segmentation based on color and local homogeneity features. in this paper, we propose an improved approach for image segmentation based on color and local homogeneity features. a given image is transformed into a quantized image by a self-constructing fuzzy clustering. then, a color-based region image and an initial seeded region image are obtained from the quantized image by color-based and homogeneity-based region growing methods, respectively. after that, we combine these two images to generate a refined seeded region image and obtain an initial segmented image by a region-based region growing. finally, merging based on color similarities and sizes of regions is performed for avoiding the problem of over-segmentation. compared with the other method, experimental results show that the segmented regions obtained by our approach are more reasonable and precise.
latent topic modelling of word co-occurence information for spoken document retrieval. in this paper, we present a word topic model (wtm) approach, discovering the co-occurrence relationship between words as well as the long-span latent topic information, for spoken document retrieval (sdr). a given document as a whole is modeled as a composite wtm model for generating an observed query. the underlying characteristics and different kinds of model structures are extensively investigated, while the performance of wtm is thoroughly analyzed and verified by comparison with a few existing retrieval models on the tdt-2 sdr task. we also attempt to incorporate part-of-speech (pos) weighting into the representations of the query observations and the wtm models for obtaining better retrieval performance.
parameter estimation of non-rayleigh rcs models for sar images based on the mellin transformation. the mellin transformation-based method is developed to estimate the parameters of non-rayleigh radar cross section (rcs) models for synthetic aperture radar (sar) images from the observed image. models investigated include heavy-tailed rayleigh and weibull. for each model, we consider the three kinds of images: intensity, square-root of intensity, and multi-look averaged amplitude. using the mellin transformation, we derive the analytical expressions of the first two second-kind cumulants for speckle and rcs respectively, and obtain the estimators according to the multiplicative model of sar images and the mellin convolution. results of parameter estimation from monte carlo simulation and real sar images demonstrate that the proposed estimators, which are easy to implement in the form of closed expressions, are efficient in estimating the parameters of non-rayleigh rcs models from the observed sar images.
a study of pronunciation verification in a speech therapy application. techniques are presented for detecting phoneme level mispronunciations in utterances obtained from a population of impaired children speakers. the intended application of these approaches is to use the resulting confidence measures to provide feedback to patients concerning the quality of pronunciations in utterances arising within interactive speech therapy sessions. the pronunciation verification scenario involves presenting utterances of known words to a phonetic decoder and generating confusion networks from the resulting phone lattices. confidence measures are derived from the posterior probabilities obtained from the confusion networks. phoneme level mispronunciation detection performance was significantly improved with respect to a baseline system by optimizing acoustic models and pronunciation models in the phonetic decoder and applying a nonlinear mapping to the confusion network posteriors.
quadtree structured restoration algorithms for piecewise polynomial images. iterative shrinkage of sparse and redundant representations are at the heart of many state of the art denoising and deconvolution algorithms. they assume the signal is well approximated by a few elements from an overcomplete basis of a linear space. if one instead selects the elements from a nonlinear manifold it is possible to more efficiently represent piecewise polynomial signals. this suggests that image restoration algorithms based around nonlinear transformations could provide better results for this class of signals. this paper uses iterative shrinkage ideas and a nonlinear quadtree decomposition to develop image restoration algorithms suitable for piecewise polynomial images.
q-sift: efficient feature descriptors for distributed camera calibration. we consider camera self-calibration, i.e. the estimation of parameters for camera sensors, in the setting of a visual sensor network where the sensors are distributed and energy-constrained. with the objective of reducing the communication burden and thereby maximizing network lifetime, we propose an energy-efficient approach for self-calibration where feature points are extracted locally at the cameras and efficient descriptions for these features are transmitted to a central processor that performs the self-calibration. specifically, in this work we use reduced-dimensionality quantized approximations as efficient feature descriptors. the effectiveness of the proposed technique is validated through feature matching, and epipolar geometry estimation which enable self-calibration of the network.
vibe: a powerful random technique to estimate the background in video sequences. background subtraction is a crucial step in many automatic video content analysis applications. while numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of adaptability to multiple environments, noise resilience, and computation efficiency. in this paper, we present a powerful method for background extraction that improves in accuracy and reduces the computational load. the main innovation concerns the use of a random policy to select values to build a samples-based estimation of the background. to our knowledge, it is the first time that a random aggregation is used in the field of background extraction. in addition we propose a novel policy that propagates information between neighboring pixels of an image. experiment detailed in this paper show how our method improves on other widely used techniques, and how it outperforms these techniques for noisy images.
robust word boundary detection in spontaneous speech using acoustic and lexical cues. we consider the problem of word boundary detection in spontaneous speech utterances. acoustic features have been well explored in the literature in the context of word boundary detection; however, in spontaneous speech of switchboard-i corpus, we found that the accuracy of word boundary detection using acoustic features is poor (f-score ∼ 0.63). we propose a new feature - that captures lexical cues in the context of the word boundary detection problem. we show that including proposed lexical feature along with the usual acoustic features, the accuracy of the word boundary detection improves considerably (f-score ∼ 0.81). we also demonstrate the robustness of our proposed feature in presence of different noise levels for additive white and pink noise.
trajectory training considering global variance for hmm-based speech synthesis. this paper presents a novel method for training hidden markov models (hmms) for use in hmm-based speech synthesis. the primary goal of hmm parameter optimization is to ensure that parameters generated from the trained models exhibit similar properties to natural speech. in this paper, two major problems in conventional training are addressed: 1) the inconsistency between the training and synthesis optimization criterion; and 2) the over-smoothing caused by the statistical modeling process. the proposed method integrates the global variance (gv) criterion into a trajectory training method to give a unified framework for both training and synthesis which provides both a consistent optimization criterion and a closed form solution for parameter generation. the experimental results demonstrate that the proposed method yields a significant improvement in the naturalness of synthetic speech.
eeg-based emotion recognition in music listening: a comparison of schemes for multiclass support vector machine. currently, how to equip machines with the ability for properly recognizing users' felt-emotion during multimedia presentation is a growing issue. in this study we focused on the approach for recognizing music-induced emotional responses from brain activity. a comparative study was conducted to testify the feasibility of using hierarchical binary classifiers to improve the classification performance as compared with nonhierarchical schemes. according to our classification results, we not only found that using one-against-one scheme of hierarchical binary classifier results in an improvement to performance, but also established an alternative solution for emotion recognition by proposed model-based scheme depending on 2d emotion model.
on simulation of first-order auto-regressive processes with near laplace marginals. the focus of this paper is the modeling of a class of stationary non-gaussian auto-regressive processes that often find applications in statistical signal processing. we propose a general simulation procedure for constructing a time series model with a near-laplace marginal distributions. our approach is based on a class of monte carlo rejection algorithms. a theoretical analysis of the average complexity of the proposed algorithms for simulating the time series model is included.
aerial image registration using projective polar transform. image registration is an essential step in many image processing applications that need visual information from multiple images for comparison, integration or analysis. recently researchers have introduced image registration techniques using the log-polar transform (lpt) for its rotation and scale invariant properties. however, the accuracy of the approach limits by the number of samples used in the mapping process, which affects directly the computational cost. motivated by the success of lpt based approach and its limitation, we propose a novel projective polar transform (ppt) based image registration method that is robust to translation, scale, and rotation and yields high accuracy while requires low computational cost. unlike lpt that 2d interpolation is needed in the mapping process, our method uses one-to-one mapping mechanism that directly arranges image from cartesian to polar coordinate according to pre-computed ppt map. an innovative projection mechanism is proposed to reduce the image from two to one dimensional vectors. with the proposed approach, the mapping procedure is accelerated and the matching process can be performed in 1d. this results in great reduction of the computational cost.
achievable throughput approximation for rbd precoding at high snrs. in this paper, we study the achievable throughput for regularized block diagonalization (rbd) precoding in multi-user mimo broadcast channels at high snrs. by applying an analytical framework for a high snr affine approximation to capacity, we derive the multiplexing gains and the power offsets for rbd in two cases separately. in the first case, we assume that the aggregate number of receive antennas is less than or equal to the number of transmit antennas. it is found that rbd can maintain the same multiplexing gain as dirty paper coding (dpc) and block diagonalization (bd) precoding at high snrs and has a smaller power offset than bd. the sum rate differences relative to dpc and bd are analyzed and bounded as simple functions of the system parameters. in the second case, we assume that the aggregate number of receive antennas is larger than the number of transmit antennas. although rbd can still be performed, the achievable throughput is degraded with an increasing number of receive antennas. the benefit of spatial multiplexing is completely lost due to a unit spatial multiplexing gain at high snrs.
main vowel domain tone modeling with lexical and prosodic analysis for mandarin asr. the tone is a distinctive discriminative feature in mandarin chinese. often functional, yet seldom thorough are most large-scale mandarin speech recognition systems in treating tone modeling. in particular, many lack the necessary sophistication to deal with the myriad variations arising from the combination of acoustic and lexical contexts. this paper reports an attempt to account for these variabilities and to bring richer tone modeling into the ibm mandarin broadcast transcription system. in particular, we describe a system that combines the embedded approach and a novel explicit tone modeling technique characterized by a. robust tone tracking in the main-vowel domain, and b. context-dependent models with lexical and prosodic contexts. the proposed method is validated on a connected-digits set and subsequently evaluated on a large-vocabulary broadcast transcription task. it is shown that 14.8% and 5.4% relative reductions in character error rate are achieved respectively.
voice convergin: speaker de-identification by voice transformation. speaker identification might be a suitable answer to prevent unauthorized access to personal data. however we also need to provide solutions to secure transmission of spoken information. this challenge divides into two major aspects. first, the secure transmission of the content of the spoken input and second the secure transmission of the identity of the speaker. in this paper we concentrate on the latter, i.e. how to securely transmit information via voice without revealing the identity of the speaker to unauthorized listeners. in order to make the first steps toward solving this problem we study in this paper the potential of voice transformation for speaker de-identification. we use two speaker identification approaches to verify the success of de-identification with voice transformation, a gmm-based and a phonetic approach, and study different voice transformation strategies to disguise speaker identity information while preserving understandability.
statistical nonidentifiability of close emitters: maximum-likelihood estimation breakdown and its gsa analysis. we investigate the “ambiguity region” associated with erroneous maximum-likelihood (ml) direction-of-arrival (doa) estimates of closely spaced signals, near and below the “resolution limit”. we demonstrate that the general statistical analysis (gsa) technique can accurately predict the ambiguity region for a given scenario. we consider that this prediction may be used together with the barankin bound (bb) to form a more comprehensive description of maximum-likelihood estimation (mle) performance in the problematic “threshold region” where ml techniques suffer from a dramatic failure rate (“performance breakdown”).
the expected amplitude of overlapping partials of harmonic sounds. in analyzing polyphonic signals, the handling of overlapping partials is one important problem. the assumptions usually made for partial overlaps are the additivity of the linear spectrum or that of the power spectrum. in this study, the expected amplitude of two overlapping partials is derived based on the assumption that the partials overlap at the same frequency and the phase is uniformly distributed. an overlap chain rule algorithm is proposed to estimate the amplitude for the case that more than two partials overlap. the proposed algorithm has demonstrated its better accuracy over the usual two model assumptions.
information hiding for g.711 speech based on substitution of least significant bits and estimation of tolerable distortion. in this paper, we propose a novel data hiding technique for g.711 speech based on the lsb substitution method. the novel feature of the proposed method is that a low-bitrate encoder, g.726 adpcm, is used as a reference for deciding how many bits can be embedded in a sample. experiments showed that the method outperformed the simple lsb substitution method and the selective embedding method proposed by aoki. we achieved 4-kbit/s embedding with almost no subjective degradation of speech quality, and 10 kbit/s while keeping good quality.