waashk/dblp huggingface dataset:
an efficient and robust method for detecting copy-move forgery. copy-move forgery is a specific type of image tampering, where a part of the image is copied and pasted on another part of the same image. in this paper, we propose a new approach for detecting copy-move forgery in digital images, which is considerably more robust to lossy compression, scaling and rotation type of manipulations. also, to improve the computational complexity in detecting the duplicated image regions, we propose to use the notion of counting bloom filters as an alternative to lexicographic sorting, which is a common component of most of the proposed copy-move forgery detection schemes. our experimental results show that the proposed features can detect duplicated region in the images very accurately, even when the copied region was undergone severe image manipulations. in addition, it is observed that use of counting bloom filters offers a considerable improvement in time efficiency at the expense of a slight reduction in the robustness.
multiband perceptual modulation analysis, processing and synthesis of audio signals. the decomposition of audio signals into perceptually meaningful multiband modulation components opens up new possibilities for advanced signal processing. the signal adaptive analysis approach proposed in this paper will be shown to provide a powerful handle on the signal's perceptual properties: pitch, timbre or roughness can be manipulated straight forward. additionally a synthesis method is specified providing high subjective perceptual quality. furthermore, as an application example, a novel audio processing technique is proposed which changes the key mode of a given piece of music e.g. from major to minor key or vice versa.
social correlates of turn-taking behavior. the goal of this research is to infer traits about groups of people from their turn-taking behavior in natural conversation. these traits are latent attributes in a social network, whose relative frequencies we estimate from content-derived metadata. our approach is to train statistical models of turn-taking behavior using automatic labels of speech activity, and measure the association of these models with socially correlated traits. we experimentally evaluate these ideas using the switchboard-1 speech corpus, which provides speech content and metadata associated with each speaker, such as gender, age and education, as well as inferred social correlates such as willingness to participate and initiate. we show that population proportions of these socially correlated externals can be predicted with a root meansquared error of approximately 0.1 across all mixture proportions.
effect of pronounciations on oov queries in spoken term detection. the spoken term detection (std) task aims to return relevant segments from a spoken archive that contain the query terms whether or not they are in the system vocabulary. this paper focuses on pronunciation modeling for out-of-vocabulary (oov) terms which frequently occur in std queries. the std system described in this paper indexes word-level and sub-word level lattices or confusion networks produced by an lvcsr system using weighted finite state transducers (wfst).we investigate the inclusion of n-best pronunciation variants for oov terms (obtained from letter-to-sound rules) into the search and present the results obtained by indexing confusion networks as well as lattices. the following observations are worth mentioning: phone indexes generated from sub-words represent oovs well and too many variants for the oov terms degrade performance if pronunciations are not weighted.
optimal and suboptimal micro-doppler estimation schemes using carrier diverse doppler radars. carrier diverse radars employing two different frequencies, termed as dual-frequency radars, prove effective in determining the target range in urban sensing and through-the-wall applications. in this paper, we derive the maximum likelihood (ml) estimator for the dual frequency radar returns for a micro-doppler motion profile, which is commonly exhibited by indoor moving targets. unlike linear models, the respective ml estimator does not have a closed form. we solve the ml estimator for dual frequency radar operations, using iterative reweighted least squares (irls). the ml-irls algorithm is applied to experimental radar returns for estimating the motion parameters of indoor targets.
an analysis of the map seeking circuit and monte carlo extensions. the map seeking circuit (msc) has been suggested to address the inverse problem of transformation discovery as found in signal processing, vision, inverse kinematics and many other natural tasks. according to this idea, a parallel search in the transformation space of a high dimensional problem can be decomposed into parts efficiently using the ordering property of superpositions. deterministic formulations of the circuit have been suggested. here, we provide a probabilistic interpretation of the architecture whereby the superpositions of the circuit are seen as a series of marginalisations over parameters of the transform. based on this, we interpret the weights of the msc as importance weights. the latter suggests the incorporation of monte-carlo approaches in the msc, providing improved resolution of parameter estimates within resource constrained implementations. as a final contribution, we model mixed serial/parallel search strategies of biological vision to reduce the problem of collusions, a common problem in the standard msc approach.
polyphonic musical instrument recognition based on a dynamic model of the spectral envelope. we propose a new method for detecting the musical instruments that are present in single-channel mixtures. such a task is of interest for audio and multimedia content analysis and indexing applications. the approach is based on grouping sinusoidal trajectories according to common onsets, and comparing each group's overall amplitude evolution with a set of pre-trained probabilistic templates describing the temporal evolution of the spectral envelopes of a given set of instruments. classification is based on either an euclidean or a probabilistic definition of timbral similarity, both of which are compared with respect to detection accuracy.
two-dimensional phase unwrapping using semidefinite relaxation. in many imaging applications, the continuous phase information of the measured signal is wrapped to a single period of 2π, resulting in phase ambiguity. in this paper we consider the two-dimensional phase unwrapping problem and propose a maximum a posteriori (map) framework for estimating the true phase values based on the wrapped phase data. in particular, assuming a joint gaussian prior on the original phase image, we show that the map formulation leads to a binary quadratic minimization problem. the latter can be efficiently solved by semidefinite relaxation (sdr). we compare the performances of our proposed method with the existing l1/l2-norm minimization approaches. the numerical results demonstrate that the sdr approach significantly outperforms the existing phase unwrapping methods.
an information geometric approach to supervised dimensionality reduction. due to the curse of dimensionality, high-dimensional data is often pre-processed with some form of dimensionality reduction for the classification task. many common methods of supervised dimensionality reduction have focused on separating and collapsing the data near the class centroids. these methods often make assumptions on the distributions of the data classes - namely gaussianity - which can lead to ad-hoc and sub-optimal implementation. in this paper we present a method of supervised dimensionality reduction which takes an information-geometric approach by maximizing the between class information distances. this is shown to have direct relation to the chernoff and bhattacharya performance bounds for classification error. we illustrate our methods on real data and compare to several existing methods.
learning the basic units in american sign language using discriminative segmental feature selection. the natural language for most deaf signers in the united states is american sign language (asl). asl has internal structure like spoken languages, and asl linguists have introduced several phonemic models. the study of asl phonemes is not only interesting to linguists, but also useful for scalability in recognition by machines. since machine perception is different than human perception, this paper learns the basic units for asl directly from data. comparing with previous studies, our approach computes a set of data-driven units (fenemes) discriminatively from the results of segmental feature selection. the learning iterates the following two steps: first apply discriminative feature selection segmentally to the signs, and then tie the most similar temporal segments to re-train. intuitively, the sign parts indistinguishable to machines are merged to form basic units, which we call asl fenemes. experiments on publicly available asl recognition data show that the extracted data-driven fenemes are meaningful, and recognition using those fenemes achieves improved accuracy at reduced model complexity.
spoken language understanding from unaligned data using discriminative classification models. while data-driven methods for spoken language understanding reduce maintenance and portability costs compared with handcrafted parsers, the collection of word-level semantic annotations for training remains a time-consuming task. a recent line of research has focused on building generative models from unaligned semantic representations, using expectation-maximisation techniques to align semantic concepts. this paper presents an efficient, simple technique that parses a semantic tree by recursively calling discriminative semantic classification models. results show that it outperforms methods based on the hidden vector state model and markov logic networks, while performance is close to more complex grammar induction techniques. we also show that our method is robust to speech recognition errors, by improving over a handcrafted parser previously used for dialogue data collection.
two-directional two-dimensional discriminant locality preserving projections for image recognition. we propose in this paper an improved manifold learning method called two-directional two-dimensional discriminant locality preserving projections, (2d)2-dlpp, for efficient image recognition. as the existing method of two-dimensional discriminant locality preserving projections (2d-dlpp) mainly relies upon the local structure information in the rows of images, we first derive an alternative 2d-dlpp algorithm that makes use of the information in the columns. exploiting the local structure and discriminant information in both the rows and the columns, we develop the (2d)2-dlpp method for efficient image feature extraction and dimensionality reduction. experimental results on two benchmark image datasets show the effectiveness of the proposed method.
hierarchical fusion of color and depth information at partition level by cooperative region merging. a high level scheme for information fusion to create hierarchical region-based image representations based on a region merging process is presented. the strategy is based on an iterative evolution where the different merging criteria work independently and cooperate at the partition level to obtain a further consensus that increases the reliability of the resulting partitions. this cooperative scheme is applied to the creation of hierarchical region-based representations of the image based on color and depth information. the proposed technique is compared with approaches using only one source of information or linear combinations of both, in datasets with ground truth as well as estimated disparity information.
automatic evaluation of spoken english fluency. this paper presents a method to automatically quantify the spoken english fluency skills of speakers. the focus of this work is to automatically compute a numeric score of spoken fluency that is correlated with the numerical score the human assessors would assign. the proposed method combines several novel prosodic and lexical features to compute the fluency score. it is shown that the prosodic and the lexical features provide complementary information for fluency evaluation. extensive evaluation on human-labeled utterances shows that the proposed technique exhibits similar trends in performance and confusions as shown by human assessors. the proposed technique leads to 84.2% classification accuracy when the two extreme classes of fluency are considered.
doa estimation of quasi-stationary signals via khatri-rao subspace. this paper addresses the problem of direction-of-arrival (doa) estimation of quasi-stationary signals, which finds applications in array processing of speech and audio. by studying the subspace structures of the local second-order statistics (soss) of quasi-stationary signals, we develop a khatri-rao (kr) subspace approach that has two notable advantages. first, the approach can operate in underdetermined cases. it is proven that if n is the number of sensors in the array, then the proposed approach can identify up to 2n − 2 source doas in an unambiguous fashion. second, the approach can handle the problem of unknown noise covariance. essentially, the kr subspace formulation is found to provide a simple and effective way of annihilating the (unknown) noise covariance from the observed signal soss. simulation results, with an emphasis on underdetermined and colored-noise cases, illustrate that the kr subspace approach provides promising mean square estimation error performance.
fast bayesian compressive sensing using laplace priors. in this paper we model the components of the compressive sensing (cs) problem using the bayesian framework by utilizing a hierarchical form of the laplace prior to model sparsity of the unknown signal. this signal prior includes some of the existing models as special cases and achieves a high degree of sparsity. we develop a constructive (greedy) algorithm resulting from this formulation where necessary parameters are estimated solely from the observation and therefore no user-intervention is needed. we provide experimental results with synthetic 1d signals and images, and compare with the state-of-the-art cs reconstruction algorithms demonstrating the superior performance of the proposed approach.
sampling signals with finite rate of innovation in the presence of noise. recently, it has been shown that it is possible to sample non-bandlimited signals that possess a limited number of degrees of freedom and uniquely reconstruct them from a finite number of uniform samples. these signals include, amongst others, streams of diracs. in this paper, we investigate the problem of estimating the innovation parameters of a stream of diracs from its noisy samples taken with polynomial or exponential reproducing kernels. for the one-dirac case, we provide exact expressions for the cramér- rao bounds of this estimation problem. furthermore, we propose methods to reconstruct the location of a single dirac that reach the optimal performance given by the unbiased cramér-rao bounds down to noise levels of 5db.
a trust region based optimization for maximum mutual information estimation of hmms in speech recognition. in this paper, we present a new optimization method for mmie-based discriminative training of hmms in speech recognition. in our method, the mmie training of gaussian mixture hmms is formulated as a so-called trust region problem, where a quadratic objective function is minimized under a spherical constraint, so that an efficient global optimization method for the trust region problem can be used to solve the mmie training problem of hmms. experimental results on the wsj0 nov'92 evaluation task demonstrate that the trust region based optimization significantly outperforms the conventional ebwmethod in terms of optimization convergence behavior as well as speech recognition performance. it has been observed that the trust region method achieves up to 23.3% relative recognition error reduction over a well-trained mle system while the ebw method gives only 13.3% relative error reduction.
separable pca for image classification. as an alternative to standard pca, matrix-based image dimensionality reduction methods have recently been proposed and have gained attention due to reported computational efficiency and robust performance in classification. we unify all of these methods through one concept: separable principle component analysis (spca).we show that the proposed matrix methods are either equivalent to, special cases of, or approximations to spca. we include performance comparisons of the methods on two face data sets and a handwritten digit data set. the empirical results indicate that two existing methods, bd-pca and its variant nglram, are very good, efficiently computable, approximate solutions to practical spca problems.
an efficient mispronounciation detction method using glds-svm and formant enhanced features. mispronunciation detection is an important component in computer assisted language learning (call) system. in this work, we introduce an efficient glds-svm based detection method, which is successfully used in language and speaker identification systems, and combine it with traditional methods. the main ideas include: extended mfcc features with normalized formant trajectory information, and then propose a novel multi-model strategy for model training to make full use of samples and solve the problem of data unbalance, finally combine glds-svm method with ubm-gmm system to further improve the performance. experiments show that glds-svm is highly efficient than traditional rbf-svm, and the fused system can achieve a significant relative improvement of 17.5% in eer reduction, compared with the baseline ubm-gmm system.
identification of neurons participating in cell assemblies. chances to detect assembly activity are expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, because it is often infeasible to extend methods developed for smaller data sets due to a combinatorial explosion. by evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. in this contribution, we present approaches to actually identify the individual neurons involved in assemblies. our results may complement other methods and also provide the opportunity for a reduction of data sets to the “relevant” neurons, thus allowing us to carry out a refined analysis of the detailed correlation structure due to reduced computation time.
a variational framework for simultaneous motion and disparity estimation in a sequence of stereo images. in this paper, we present a variational framework for joint disparity and motion estimation in a sequence of stereo images. the problem involves the estimation of four dense fields: two motion fields and two disparity fields. in order to reduce computational complexity and improve estimation accuracy, the two motion fields, for the left and right sequences, and the disparity field of the current stereo pair are jointly estimated, using the stereo-motion consistency constraint. in the proposed variational framework, the joint estimation problem is formulated as a convex programming problem in which a convex objective function is minimized under specific convex constraints. this minimization is achieved using an efficient parallel block-iterative algorithm. experimental results involving real stereo sequences indicate the feasibility and robustness of our approach.
syntactically-informed models for comma prediction. providing punctuation in speech transcripts not only improves readability, but it also helps downstream text processing such as information extraction or machine translation. in this paper, we improve by 7% the accuracy of comma prediction in english broadcast news by introducing syntactic features inspired by the role of commas as described in linguistics studies. we conduct an analysis of the impact of those features on other subsets of features (prosody, words…) when combined through crfs. the syntactic cues can help characterizing large syntactic patterns such as appositions and lists which are not necessarily marked by prosody.
efficient semidefinite relaxation for energy-based source localization in sensor networks. recently, energy-based localization using acoustic energy measurements has received much attention in wireless sensor networks. since the objective function of the energy-based maximum likelihood (ml) localization is non-convex, the global solutions are hardly obtained without good initial estimates. in this paper, we relax this non-convex problem as a convex semidefinite programming (sdp), based on which a good estimate can be obtained and be improved by a procedure called randomization. simulation results show that the proposed method is effective and outperforms the existing methods.
rate efficient remote video file synchronization. video file synchronization between remote users is an important task in many applications. re-transmission of a video that has been only slightly modified is expensive, wasteful and avoidable. we propose a scheme that automatically detects and sends only modified content according to some user defined distortion constraint to enable rate savings under a wide range of video edits. through the use of a low-rate hierarchical hashing scheme, we can detect modifications with some spatial granularity. we also apply distributed source coding techniques to exploit correlation between remote copies for a further rate rebate. experimental results show that the proposed approach achieves up to 7× rate reduction when compared to re-transmitting.
incremental predictive and adaptive noise compensation. model compensation schemes are a powerful approach to handling mismatches between training and testing conditions. normally these schemes are run in a batch adaptation mode, re-recognising the utterance used to estimate the noise model parameters. for many applications this introduces unacceptable latency. this paper examines three forms of incremental mode model-based compensation: vector taylor series; joint uncertainty decoding; and predictive cmllr. these predictive schemes can also be combined with adaptive schemes such as cmllr. by combining the approaches, weaknesses of each can be addressed. the performance is evaluated on in-car recorded data, where the combined incremental scheme shows gains over either individually.
classification of electroencephalography (eeg) signals for different mental activities using kullback leibler (kl) divergence. automatic classification of electroencephalography (eeg) signals, for different type of mental activities, is an active area of research and has many applications such as brain computer interface (bci) and medical diagnoses. we introduce a simple yet effective way to use kullback-leibler (kl) divergence in the classification of raw eeg signals. we show that k-nearest neighbor (k-nn) algorithm with kl divergence as the distance measure, when used using our feature vectors, gives competitive classification accuracy and consistently outperforms the more commonly used euclidean k-nn. we also develop and demonstrate the use of a kl-based kernel to classify eeg data using support vector machines (svms). our kl-distance based kernel compares favorably to other well established kernels such as linear and radial basis function (rbf) kernel. the eeg data, used in our experiments for classification, was recorded while the subject performed 5 different mental activities such as math problem solving, letter composing, 3-d block rotation, counting and resting (baseline). we present classification results for this data set that are obtained by using raw eeg data with no explicit artifact removal in the pre-processing steps.