waashk/dblp huggingface dataset:
grouping motion trajectories. we present a method to group trajectories of moving objects extracted from real-world surveillance videos. the trajectories are first mapped into a low dimensionality feature space generated through linear regression. next the regression coefficients are clustered by a gaussian mixture model initialized by k-means for improved efficiency. the model selection problem is solved with bayesian information criterion that penalizes models with high complexity. we demonstrate the proposed approach on both synthetic and real-world scenes. experimental results show that the proposed clustering method outperforms k-means and mixture of regression models, while also reducing the computational complexity compared to the latter.
an improved satd-based intra mode decision algorithm for h.264/avc. in the previous work [3] we presented a satd-based intra mode decision algorithm for h.264/avc to reduce the computational complexity, based upon satd coefficients. in this paper, we extend the previous work and propose an improved satd-based intra mode decision, which uses low-frequency components to provide local characteristics for intra mode selection. the experimental results reveal that our proposed algorithm can achieve significant reduction of computation time compared to the reference program, while still maintaining good coding performance. the proposed algorithm is also compared with two distinct algorithms and the results indicate that the proposed algorithm brings out higher reduction in computation that these two algorithms.
probabilistic matrix tri-factorization. nonnegative matrix tri-factorization (nmtf) is a 3-factor decomposition of a nonnegative data matrix, x ≈ usv┬, where factor matrices, u, s, and v , are restricted to be nonnegative as well. motivated by the aspect model used for dyadic data analysis as well as in probabilistic latent semantic analysis (plsa), we present a probabilistic model with two dependent latent variables for nmtf, referred to as probabilistic matrix tri-factorization (pmtf). each latent variable in the model is associated with the cluster variable for the corresponding object in the dyad, leading the model suited to co-clustering. we develop an em algorithm to learn the pmtf model, showing its equivalence to multiplicative updates derived by an algebraic approach. we demonstrate the useful behavior of pmtf in a task of document clustering. moreover, we incorporate the likelihood in the pmtf model into existing information criteria so that the number of clusters can be detected, while the algebraic nmtf cannot.
recursive least-squares decision-directed tracking of doubly-selective channels using exponential basis models. we present a decision-directed tracking approach to doubly-selective channel estimation exploiting the complex exponential basis expansion model (ce-bem). the time-varying nature of the channel is well captured by the ce-bem while the time-variations of the (unknown) bem coefficients are likely much slower than those of the channel. we track the bem coefficients via the exponentially-weighted recursive least-squares (rls) algorithm, aided by symbol decisions from a decision-feedback equalizer (dfe). simulation examples demonstrate its superior performance over an existing subblock-wise channel tracking scheme.
modified filtered-x dichotomous coordinate descent recursive affine projection algorithm. in this paper, we propose a new multichannel filtered-x affine projection algorithm based on dichotomous coordinate descent (dcd) iterations for active noise control (anc) systems. it includes a fast recursive filtering procedure with the filter update incorporated in the dcd iterations. it is shown that the proposed algorithm has a lower complexity, and superior convergence properties than the multichannel filtered-x lms algorithm. also, it compares favorably to a previously published dcd based algorithm for anc systems.
structured variational methods for distributed inference in wireless ad hoc and sensor networks. in this paper, a variational message passing framework is proposed for markov random fields, which is computationally more efficient and admits wider applicability compared to the belief propagation algorithm. based on this framework, structured variational methods are explored to take advantage of both the simplicity of variational approximation (for inter-cluster processing) and the accuracy of exact inference (for intra-cluster processing). its performance is elaborated on a gaussian markov random field, through both theoretical analysis and simulation results.
idempotent h.264 intraframe multi-generation coding. this paper studies the idempotence of h.264 intraframe multi-generation coding. we analyze the h.264 transform and quantization and reveal that for some quantization parameters there always exists at least one clipping compensation matrix to make the h.264 intraframe multi-generation coding idempotent as long as the same prediction mode and the same quantization parameter are selected for each coding generation. in addition, an idempotent h.264 intraframe multi-generation coding procedure is presented.
band clustering and selection and decision fusion for target detection in hyperspectral imagery. a band clustering and selection approach based on a hyperspectral measure, spectral information divergence (sid) is presented in this paper. hyperspectral image data is analyzed for target detection. hyperspectral image data and spectral signatures of the targets are used to measure the sid. virtual dimensionality (vd) is used to select optimal number of bands. for endmember extraction, vertex component analysis (vca) is used. for decision fusion a new approach based on spectral discriminatory entropy (sde) is proposed. a comparative study is conducted to show the effectiveness of new approach of band clustering and selection. decision fusion is also compared with full band and individual sid detection schemes.
modeling instantaneous intonation for speaker identification using the fundamental frequency variation spectrum. in recent years, the field of automatic speaker identification has begun to exploit high-level sources of speaker-discriminative information, in addition to traditional models of spectral shape. these sources include pronunciation models, prosodic dynamics, pitch, pause, and duration features, phone streams, and conversational interaction. as part of this broader thrust, we explore a new frame-level vector representation of the instantaneous change in fundamental frequency, known as fundamental frequency variation (ffv). the ffv spectrum consists of 7 continuous coefficients, and can be directly modeled in a standard gaussian mixture model (gmm) framework. our experiments indicate that ffv features contain useful information for discriminating among speakers, and that model-space combination of ffv and cepstral features outperforms cepstral features alone. in particular, our results on 16khz wall street journal data show relative reductions in error rate of 54% and 40% for female and male speakers, respectively.
robust cross-race gene expression analysis. this paper develops a bayesian network (bn) predictor to profile cross-race gene expression data. cross-race studies face more data variability than single-lab studies. our design handles this problem by using the bn framework. in addition, unlike existing methods that unrealistically assume independent genes, our bn approach can capture the dependencies among genes. existing bn algorithms in biomedicine applications quantize data, leading to information loss; we adopt linear gaussian model to keep the data intact, so our resulting model is more reliable. the application of our bn predictor to a lung adenocarcinoma study shows high prediction accuracy, and performance evaluation demonstrates our gene signature agreeable with those reported in the literature. our tool has a promising potential in finding disease biomarkers common to multiple races.
the australian english speech corpus for in-car speech processing. the australian in-car speech corpus is a multi-channel recording of a series of prompts from an in-car navigation task collected over a range of speakers in a variety of driving conditions. its purpose is to provide a significant resource of speech data appropriate for investigating speech processing needs in the adverse environment of a car. utterances spoken by 50 speakers were collected in seven different driving conditions, providing the foundation for investigation into noisy, speaker-independent speech processing. speech recognition experiments are performed to validate the data, to provide baseline results for in-car speech recognition research, and to show that this data can improve speech recognition performance under adverse in-car conditions for australian english when adapting from american english acoustic models.
a bayesian approach to spectrum sensing, denoising and anomaly detection. this paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. the focus is on the case when the variance of the noise is unknown. we derive the optimal soft decision detector using a bayesian approach. the complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. the problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. we illustrate the results in the context of the latter.
voice search of structured media data. this paper addresses the problem of using unstructured queries to search a structured database in voice search applications. by incorporating structural information in music metadata, the end-to-end search error has been reduced by 15% on text queries and up to 11% on spoken queries. based on that, an hmm sequential rescoring model has reduced the error rate by 28% on text queries and up to 23% on spoken queries compared to the baseline system. furthermore, a phonetic similarity model has been introduced to compensate speech recognition errors, which has improved the end-to-end search accuracy consistently across different levels of speech recognition accuracy.
learning to maximize signal-to-noise ratio for reverberant speech segregation. monaural speech segregation in reverberant environments is a very difficult problem. we develop a supervised learning approach by proposing an objective function that directly relates to the computational goal of maximizing signal-to-noise ratio. the model trained using this new objective function yields significantly better results for time-frequency unit labeling. in our segregation system, a segmentation and grouping framework is utilized to form reliable segments under reverberant conditions and organize them into streams. systematic evaluations show very promising results.
voxel selection in fmri data analysis: a sparse representation method. this paper proposes an iterative sparse representation-based algorithmfor voxel selection in functionalmagnetic resonance imaging (fmri) data. the output of the algorithm is a sparse weight vector, of which the magnitude of each entry represents the significance of its corresponding voxel with respect to mental tasks or stimulus. to demonstrate the validity of our algorithm and illustrate its application, we apply this algorithm to the pittsburgh brain activity interpretation competition (pbaic) 2007 fmri data set for selecting the voxels which are the most relevant to the tasks of the subjects. compared with three baseline methods, general linear model (glm)-based statistical parametric mapping (spm), correlation method and mutual information method, our method shows satisfactory performance for voxel selection.
a robust harmony structure modeling scheme for classical music opus identification. a robust algorithm to model the harmony structure of a music piece is proposed. the harmony structure is extracted directly from a music audio signal using a second-order statistic of chroma feature vectors. the method is experimentally shown to be robust against the degradation of chroma feature vectors due to noisy pitch estimation in our classical music opus identification evaluation. to analyze the effects of the noisy pitch estimation, we propose a noise model that describes difference between the oracle chroma feature vectors as obtained from a symbolic representation and those extracted from the rendered audio signal. the results suggest that the harmony structure modeling scheme employing the covariance matrix is more robust than the alternative investigated second-order statistics. the results also show that the proposed method obtains 84.3% accuracy with the symbolic representations and 72.0% with the synthesized audio data, which suggest that the proposed harmony structure modeling method has room for further improvement by addressing the signal processing challenges of pitch extraction, or through employing more robust features.
recent improvements of probability based prosody models for unit selection in concatenative text-to-speech. the work presented in this paper is subsequent to the paper “probability based prosody model for unit selection” which was published in icassp'2004. in the improved probability prosody model for corpus based concatenative text-to-speech (tts), likelihood is replaced with posterior probability in the cost functions which conduct the following step, unit selection. objective and subjective experiments show that posterior probability has obvious advantages over likelihood on robustness, flexibility and overall quality.
billiards wizard: a tutoring system for broadcasting nine-ball billiards videos. in this work, we propose a framework to build a billiards tutoring system based on broadcasting nine-ball video analysis. a robust table detection module is developed by mapping the displayed video frames to a predefined billiard table model. in addition, we detect balls and trace their positions at every time instant. the real-world spatial relationships between the table and the balls are used to provide the aiming and position play suggestions. ball position information is also utilized to distinguish each play into corresponding event by a rule-based method. the experimental results are encouraging and are more comprehensive than existing works of billiards video analysis.
risk-distortion analysis for video collusion attack. collusion attack is a cost-effective attack against digital fingerprint. to develop an efficient collusion-resistant fingerprint scheme, it is very important for the detector to study the behavior of the colluders and the performance of collusion attack. although several prior works have been proposed in the literature to analyze the performance of collusion attack, few effort has been made to explicitly study the relationship between risk, i.e., the probability of the colluders to be detected, and distortion of collusion attack. in this paper, we investigate the risk-distortion relationship of the linear video collusion attack with gaussian fingerprint. we formulate the optimal linear collusion attack as an optimization problem, where the colluders try to minimize the distortion subject to a risk constraint. for any fixed risk constraint, the optimal distortion can be found using numerical optimization methods. by varying the risk constraint, we can obtain the risk-distortion model. we also conduct experiments to verify the proposed risk-distortion model using real video data.
a joint decoding algorithm for multiple-example-based addition of words to a pronunciation lexicon. we propose an algorithm that enables joint viterbi decoding of multiple independent audio recordings of a word to derive its pronunciation. experiments show that this method results in better pronunciation estimation and word recognition accuracy than that obtained either with a single example of the word or using conventional approaches to pronunciation estimation using multiple examples.
long-time span acoustic activity analysis from far-field sensors in smart homes. smart homes for the aging population have recently started attracting the attention of the research community. one of the problems of interest is this of monitoring the activities of daily living (adls) of the elderly, in order to help identify critical problems, aiming to improve their protection and general well-being. in this paper, we report on our initial attempts to recognize such activities, based on input from networks of far-field microphones distributed inside the home. we propose two approaches to the problem: the first models the entire activity, which typically covers long time spans, with a single statistical model, for example a hidden markov model (hmm), a gaussian mixture model (gmm), or gmm super-vectors in conjunction with support vector machines (svms). the second is a two-step approach: it first performs acoustic event detection (aed) to locate distinctive events, characteristic of the adls, and it is subsequently followed by a post-processing stage that employs activity-specific language models (lms) to classify the output sequences of detected events into adls. experiments are reported on a corpus containing a small number of acted adls, collected as part of the netcarity integrated project inside a two-room smart home. our results show that svm gmm supervector modeling improves six-class adl classification accuracy to 76%, compared to 56% achieved by the gmms, while also outperforming hmms by 8% absolute. preliminary results from lm scoring of acoustic event sequences are comparable to those from gmms on a three-class adl classification task.
a fast and efficient algorithm for low rank matrix recovery from incomplete observations. minimizing the rank of a matrixx over certain constraints arises in diverse areas such as machine learning, control system and is known to be computationally np-hard. in this paper, a new simple and efficient algorithm for solving this rank minimization problem with linear constraints is proposed. by using gradient projection method to optimize s while consecutively updating matrices u and v (where x = usvt ) in combination with the use of an approximation function for l0-norm of singular values [1], our algorithm is shown to run significantly faster with much lower computational complexity than general-purpose interior-point solvers, for instance, the sedumi package [2]. in addition, the proposed algorithm can recover the matrix exactly with much fewer measurements and is also appropriate for large-scale applications.
transcoding based robust streaming of compressed video. a variety of techniques have been proposed to enhance the error robustness of the video streaming system. however, most of them improves the error resilience during compression rather than after compression. in this paper, we propose a novel transcoding based scheme called lossless inter frame transcoding (lift) scheme to improve the error resilience of existing compressed video stream. in the lift scheme, inter coded blocks are selectively transcoded into new kind of blocks called ‘l-block’. at the decoder, the l-block can be transcoded back to the original p-block when the prediction is available and can also be robustly decoded as i-block when the prediction is unavailable. by offline transcoding and online adjusting the ratio of p-blocks and l-blocks, the proposed streaming server achieves error robustness scalability. experimental results demonstrate the correctness and effectiveness of the proposed method.
structured least squares with bounded data uncertainties. in many signal processing applications the core problem reduces to a linear system of equations. coefficient matrix uncertainties create a significant challenge in obtaining reliable solutions. in this paper, we present a novel formulation for solving a system of noise contaminated linear equations while preserving the structure of the coefficient matrix. the proposed method has advantages over the known structured total least squares (stls) techniques in utilizing additional information about the uncertainties and robustness in ill-posed problems. numerical comparisons are given to illustrate these advantages in two applications: signal restoration problem with an uncertain model and frequency estimation of multiple sinusoids embedded in white noise.
a global optimization framework for meeting summarization. we introduce a model for extractive meeting summarization based on the hypothesis that utterances convey bits of information, or concepts. using keyphrases as concepts weighted by frequency, and an integer linear program to determine the best set of utterances, that is, covering as many concepts as possible while satisfying a length constraint, we achieve rouge scores at least as good as a rouge-based oracle derived from human summaries. this brings us to a critical discussion of rouge and the future of extractive meeting summarization.
color extended visual cryptography using error diffusion. this paper introduces a color visual cryptography encryption method that produces meaningful color shares via visual information pixel (vip) synchronization and error diffusion halftoning. vip synchronization retains the positions of pixels carrying visual information of original shares throughout the color channels and error diffusion generates shares pleasant to human eyes. comparisons with previous approaches show the superior performance of the new method.
you are fired! nonverbal role analysis in competitive meetings. this paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. for our study, we made use of “the apprentice” reality tv show, which features a competition for a real, highly paid corporate job. our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. the current study was carried out using nonverbal audio cues. results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words.
multi-flow attack resistant watermarks for network flows. in this work we present a multi-flow attack resistant interval centroid based watermarking (mar-icbw) scheme for network flows. our proposed scheme can withstand the newly introduced multi-flow watermarking attack that defeats the state-of-the-art interval-based network flow watermarking schemes. multi-flow attack uses the dependent correlations among the flows marked with the same watermark to recover the secret parameters, and remove the watermark from a flow. the attack can be effective even if different flows are marked with different values of a watermark. mar-icbw survives the attack by virtue of randomizing the location of the embedded watermark across multiple flows and therefore, effectively removing the correlations between the flows. while we represent our counter measure to multi-flow attack in terms of an improved version of icbw, the same methodology can be used to strengthen other interval-based flow watermarking schemes.