waashk/dblp huggingface dataset:
enhanced error resilience of video communications for burst losses using an extended rope algorithm. video communications over wireless networks suffers various patterns of losses, including burst losses that cause great degradation in video quality. in this paper, we propose an algorithm based on recursive optimal per-pixel estimate (rope) to accurately estimate the overall distortion accounting for the loss pattern. the estimated distortion is applied to the rate-distortion (rd)-based mode selection to provide the optimal tradeoff between intra and inter coding. simulation results show that in lossy networks, the proposed extended rope algorithm achieves gains in average psnr and psnrr,f by up to 0.8 db and 3.6 db respectively. this shows that the proposed algorithm can enhance error resilience for video communications over networks with burst losses.
connecting spectral and spring methods for manifold learning. diffusion maps (diffmaps) has recently provided a general framework that unites many other spectral manifold learning algorithms, including laplacian eigenmaps, and it has become one of the most successful and popular frameworks for manifold learning to date. however, diffusion maps still often creates unnecessary distortions, and its performance varies widely in response to parameter value changes. in this paper, we draw a previously unnoticed connection between diffmaps and spring-motivated methods. we show that diffmaps has a physical interpretation: it finds the arrangement of high-dimensional objects in low-dimensional space that minimizes the elastic energy of a particular spring network. within this interpretation, we recognize the root cause of a variety of problems that are commonly observed in the diffusion maps output, including sensitivity to user-specified parameters, sensitivity to sampling density, and distortion of boundaries. we then show how to exploit the connection between diffusion map and spring criteria to create a method that can be efficiently applied post hoc to alleviate these commonly observed deficiencies in the diffusion maps output.
dirichlet process mixture models with multiple modalities. the dirichlet process can be used as a nonparametric prior for an infinite-dimensional probability mass function on the parameter space of a mixture model. the set of parameters over which it is defined is generally used for a single, parametric distribution. we extend this idea to parameter spaces that characterize multiple distributions, or modalities. in this framework, observations containing multiple, incompatible pieces of information can be mixed upon, allowing for all information to inform the final clustering result. we provide a general mcmc sampling scheme and demonstrate this framework on a gaussian-hmm mixture model applied to synthetic and major league baseball data.
component-wise pose normalization for pose-invariant face recognition. the pose variation involved in facial images significantly degrades the performance of face recognition systems. in this paper, a component-wise pose normalization method for facilitating pose-invariant face recognition is proposed. the main idea is to normalize a non-frontal facial image to a virtual frontal image component by component. in this method, we first partition the whole non-frontal facial image into different facial components and then the virtual frontal view for each component is estimated separately. the final virtual frontal image is generated by integrating the virtual frontal components. the proposed method relies only on 2d images, therefore complex 3d modeling is not needed. the experimental results using the cmu-pie database demonstrate the advantages of the proposed method.
expectation-maximization algorithm for multi-pitch estimation and separation of overlapping harmonic spectra. this paper addresses the problem of multi-pitch estimation, which consists in estimating the fundamental frequencies of multiple harmonic sources, with possibly overlapping partials, from their mixture. the proposed approach is based on the expectation-maximization algorithm, which aims at maximizing the likelihood of the observed spectrum, by performing successive single-pitch and spectral envelope estimations. this algorithm is illustrated in the context of musical chord identification.
pilot-assisted channel estimation for mimo ofdm systems using theory of sparse signal recovery. in this work, a new framework for channel estimation in mimo ofdm systems is provided. sparse channel estimation refers to estimating the time domain channel impulse response by exploiting the fact that the channel has a very few nonzero taps. we formalize the problem and drive necessary and sufficient condition on the number of pilots for perfect channel recovery which leads to a l0 norm optimization problem. a practical suboptimal solution is proposed that is a modified orthogonal matching pursuit (omp) which exploits the sparsity structure of the mimo channel. the investigations reveal that the training overhead can be drastically reduced while maintaining the same accuracy as the current state of the art techniques.
posterior-based confidence measures for spoken term detection. confidence measures play a key role in spoken term detection (std) tasks. the confidence measure expresses the posterior probability of the search term appearing in the detection period, given the speech. traditional approaches are based on the acoustic and language model scores for candidate detections found using automatic speech recognition, with bayes' rule being used to compute the desired posterior probability. in this paper, we present a novel direct posterior-based confidence measure which, instead of resorting to the bayesian formula, calculates posterior probabilities from a multi-layer perceptron (mlp) directly. compared with traditional bayesian-based methods, the direct-posterior approach is conceptually and mathematically simpler. moreover, the mlp-based model does not require assumptions to be made about the acoustic features such as their statistical distribution and the independence of static and dynamic co-efficients. our experimental results in both english and spanish demonstrate that the proposed direct posterior-based confidence improves std performance.
a polynomial segment model based statistical parametric speech synthesis sytem. in this paper, we present a statistical parametric speech synthesis system based on the polynomial segment model (psm). as one of the segmental models for speech signals, psm explicitly describes the trajectory of the features in a speech segment, and keeps the internal dynamics of the segment. in this work, spectral and excitation parameters are modeled by psms simultaneously, while the duration for each segment is modeled by a single gaussian distribution. a top-down k-means clustering technique is applied for model tying. mean trajectories acquired from psms are used directly to generate speech parameters according to the estimated segment duration. an english speech synthesizer back-end is implemented on cmu arctic corpus and the performance of the new approach is compared with the classical hmm-based one. experimental results show that psm modeling can achieve similar naturalness and intelligence of the synthetic speech as hmm modeling. the system is in the early stage of its development.
upper bound for the loss factor of energy detection of random signals in multipath fading cognitive radios. in this paper, the loss of energy detection compared with optimal sensing caused by neglecting signal correlation due to multipath fading is considered. the loss factor or relative performance of energy detection compared with optimal sensing is analyzed using pitman's asymptotic relative efficiency (are) which is defined as the ratio of the required number of samples of one detector to that of the other to yield the same detection performance in large sample scheme. under the assumption of l-tap finite impulse response (fir) channel with zero-mean independent and identically distributed (i.i.d.) tap coefficients, it is shown that the loss factor of the energy detection relative to optimal sensing is no larger than 1/2 in large delay spread case (i.e., strong correlation); under the same signal power condition the required number of samples for energy detection neglecting the signal correlation is no more than twice of that required for optimal sensing exploiting the signal correlation fully.
a variable step size and variable tap length lms algorithm for impulse responses with exponential power profile. step size and tap length play critical roles in balancing the complexity and steady-state performance of an adaptive filter. for an impulse response with an exponential power decay profile, which models a wide range of practical systems, such as an acoustic echo path, this paper proposes a new variable step-size and tap-length least mean square (lms) algorithm. in each iteration, the optimal step-size and tap-length are derived by minimizing the mean square deviation (msd) between the true and the estimated filter coefficients. the proposed algorithm performs better in terms of both convergence rate and steady-state performance than the existing ones. effectiveness of the proposed algorithm is demonstrated through computer simulations.
multichannel spectral pattern separation - an eeg processing application -. a problem of information separation in multichannel recordings is important in engineering applications such as brain computer/machine interfaces (bci/bmi). whereas this problem is not entirely new, engineering approaches connecting the mental states of humans and the observed electroencephalography (eeg) recordings are still in their infancy, mostly due to problems with electrophysiological denoising. the electrophysiological signals captured in form of the eeg carry brain activity in form of the neurophysiological components which are usually embedded in much higher power electrical muscle activity components (electromyography - emg; electrooculography - eog; etc.). in this paper we present an approach to remove muscular interference caused by eye-movements from eeg recorded during auditory experiments in an eight channel recording setting. this is achieved by analyzing the correlation of the oscillatory modes within a multichannel signal in the hilbert domain. simulations in a real world auditory bci setting support the analysis.
robust adaptive algorithm for active noise control of impulsive noise. the paper concerns active control of impulsive noise. the most famous filtered-x least mean square (fxlms) algorithm for active noise control (anc) systems is based on the minimization of variance of error signal. the impulsive noise can be modeled using non-gaussian stable process for which second order moments do not exist. the fxlms algorithm, therefore, becomes unstable for the impulsive noise. among the existing algorithms for anc of impulsive noise, one is based on the minimizing least mean p-power (lmp) of the error signal, resulting in fxlmp algorithm. the other is based on modifying; on the basis of statistical properties; the reference signal in the update equation of the fxlms algorithm. in this paper, the proposed algorithm is a modification and combination of these two approaches. extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithm. it achieves the best performance among the existing algorithms, and at the same computational complexity as that of fxlmp algorithm.
towards automatic argument diagramming of multiparity meetings. this paper focuses on a lesser studied multiparty meetings processing task of argument diagramming. argument diagramming aims at tagging the utterances and their relationships to represent the flow and structure of reasoning in conversations, especially in discussions and arguments. in this work, we tackle the problem of automatically assigning node types to user utterances using several lexical and prosodic features. we performed experiments using the ami meeting corpus annotated according to the the twente argumentation schema. our results indicate that while lexical and prosodic features both provide orthogonal information for this task, using a cascaded approach, eliminating backchannel utterances improves the performance. with this final approach, when all features are used, we achieve about 9% relatively better error rates than a simpler classifier based on only lexical features.
performance issues in recursive least-squares adaptive gsc for speech enhancement. one fundamental non-stationary scenario involves a time-varying system in which the cross-correlation between the input signal and the desired response is time-varying. this case occurs in speech enhancement applications, where the optimal solution is time-varying due to the speech signal non-stationarity. adaptive filtering performance analysis of time-varying systems is crucial to further understand the tracking behavior and to ‘optimally’ design the update schemes. in this work, we investigate the tracking performance of the adaptive gsc applied for speech denoising. first, we interpret the noise cancellation in terms of non-stationary system identification. then, we formulate the rls adaptation as a filtering operation on the (time-varying) optimal filter and the instantaneous gradient noise (induced by the measurement noise). under some structural assumptions, we derive an expression for the excess mean squared error (emse). monte-carlo simulations show that the proposed expression allows for a good prediction of the emse, and outperforms the state-of-the-art approximations.
a robust nlms algorithm with a novel noise modeling based on stationary/nonstationary noise decomposition. this paper proposes a robust nlms algorithm with a novel noise modeling based on stationary/nonstationary noise decomposition. the ambient noise including the near-end signal is modeled as a weighted sum of the stationary and the nonstationary components. these components are independently estimated with an appropriate time constant for better accuracy. the estimates are weighted by the stationary/ nonstationary likelihood before summation. the integrated noise estimate controls the coefficient adaptation stepsize such that it is an upward convex function of the reference input with a noise offset for robustness. evaluations in a fullband and a subband echo cancellation scenarios show that erle has been improved by as much as 40 db over the algorithm with a conventional noise model in both single- and double-talk sections with no double-talk detection.
digital camera identification based on curvelet transform. in this paper, a new method is proposed for digital camera identification from its color images using image sensor noise. currently the proposed camera identification methods use wavelet-based denoising filter to extract the sensor noise feature. however, the wavelet methods may smooth the edged while denoising and this will lead to low accuracy for those images including highly textured regions. in order to overcome some inherent limitations of wavelet transform, we use curvelet-based denoising filter to obtain the camera fingerprint. experimental results show that this method provides higher accuracy than other methods on the condition of using a few color images to compute reference pattern, especially for those color images including highly textured regions.
logarithmic quantization index modulation: a perceptually better way to embed data within a cover signal. in this paper, a new method for logarithmic quantization index modulation (qim) is proposed. in this regard a logarithmic function is first applied to the host signal. then the transformed signal is quantized using uniform quantization as conventional qim to embed watermark data within. finally using inverse transform the watermarked signal is obtained. the watermark extraction is performed using minimum distance decoder. the optimum parameter for data embedding with minimum quantization distortion is derived. also the probability of error is analytically calculated and verified by simulation. furthermore data hiding using secret key is proposed and the probability of error is obtained. simulation results show that the proposed method outperforms the conventional qim in terms of robustness when the perceptual quality of watermarked image for both methods are similar. moreover, simulation shows that the proposed scheme has outstanding robustness in comparison with a recent quantization based data hiding method.
a novel pattern identification scheme using distributed video coding concepts. pattern-based video coding focusing on moving region in a macroblock has already established its superiority over recent h.264 video coding standard at very low bit rate. obviously, a large number of pattern templates approximate the moving regions better however, after a certain limit no coding gain is observed due to the increase number of pattern identification bits. recently, distributed video coding schemes used syndrome coding to predict the original information in decoder using side information. in this paper a novel pattern identification scheme is proposed which predicts the pattern from the syndrome codes and side information in decoder so that actual pattern identification number is not needed in the bitstream. the experimental results confirm that this new scheme successfully improves the rate-distortion performance compared to the existing pattern-based video coding as well as h.264 standard. this new scheme will also open another window of syndrome coding application.
why the stochastic mv-pure estimator excels in highly noisy situations? the stochastic mv-pure estimator has recently emerged as the robust solution for frequently occuring in practice problem of linear estimation in ill-conditioned and imperfectly known linear stochastic model. in this paper we provide theoretical results showing that the stochastic mv-pure estimator can be used to the greatest effect in highly noisy settings. in such settings, we discuss the relation between the stochastic mv-pure estimator and the well-known reduced rankwiener filter. we verify the theoretical results presented by a means of numerical simulations.
a near optimum detection in alpha-stable impulsive noise. alpha stable distribution has gained much attention due to its generality to represent heavy-tailed and impulsive interference. in such non-gaussian interference, the detection key is to evaluate the zero-memory nonlinearity (zmnl) function of locally optimal (lo) detector. unfortunately, there is no closed form expression for the probability density function (pdf) of alpha-stable distributions. hereby, sub-optimum zmnl function is adopted as an unavoidable approximation, such as classical cauchy and gaussian-tailed zmnl (gzmnl). in this paper, an algebraic-tailed zmnl (azmnl) with a concise form is proposed. based on such zmnl, derived detector has near optimal performance in various impulsive noise environments. furthermore, using bi-parameter cgm (bcgm), a concise approximate expression for pdf of symmetric α-stable (sαs) distribution, the test threshold can be evaluated according with preset false alarm ratio easily.
h.264/svc scene motion analysis. we present a simple and lightweight approach to scene analysis in the h.264/svc domain. the method is entirely based on the motion vectors found in the compressed stream. motion segmentation and object detection is performed after the estimation of the camera motion. important object properties are calculated, which are used for object matching and trajectory estimation. the relative distance to the camera is estimated, resulting in a pseudo 3-d representation of the object trajectories.
split convex minimization algorithm for signal recovery. a broad range of signal recovery problems can be abstracted into the problem of minimizing the sum of several convex functions in a hilbert space. we propose a proximal decomposition algorithm which, under mild conditions, provides a solution to such a problem. a significant improvement over the methods currently in use in the area of signal recovery is that it is not limited to two nondifferentiable functions. an application to image restoration is demonstrated.
fast lcd motion deblurring by decimation and optimization. the lcd deblurring problem is considered as a simple bounded quadratic programming problem and is solved using conjugate gradient with early stopping criteria to avoid excessive search. a decimation and interlace interpolation method is introduced to reduce the computing time. solutions are competitive to those the generated by conventional lucy richardson algorithm, but using much shorter amount of time. the method can be extended to higher decimation factors. visual subjective tests are conducted to justify our proposed method.
fast mean square convergence of consensus algorithms in wsns with random topologies. the average consensus in wireless sensor networks is achieved under assumptions of symmetric or balanced topology at every time instant. however, communication and/or node failures, as well as node mobility or changes in the environment make the topology vary in time, and instantaneous symmetry of the links is not guaranteed unless an acknowledgment protocol or an equivalent approach is implemented. in this paper, we evaluate the convergence in the mean square sense of a well-known consensus algorithm assuming a random topology and asymmetric communication links. a closed form expression for the mean square error of the state is derived as well as the optimum choice of parameters to guarantee fastest convergence of the mean square error.
a study on multilingual acoustic modeling for large vocabulary asr. we study key issues related to multilingual acoustic modeling for automatic speech recognition (asr) through a series of large-scale asr experiments. our study explores shared structures embedded in a large collection of speech data spanning over a number of spoken languages in order to establish a common set of universal phone models that can be used for large vocabulary asr of all the languages seen or unseen during training. language-universal and language-adaptive models are compared with language-specific models, and the comparison results show that in many cases it is possible to build general-purpose language-universal and language-adaptive acoustic models that outperform language-specific ones if the set of shared units, the structure of shared states, and the shared acoustic-phonetic properties among different languages can be properly utilized. specifically, our results demonstrate that when the context coverage is poor in language-specific training, we can use one tenth of the adaptation data to achieve equivalent performance in cross-lingual speech recognition.
mutual information based channel selection for speaker diarization of meetings data. in the meeting case scenario, audio is often recorded using multiple distance microphones (mdm) in a non-intrusive manner. typically a beamforming is performed in order to obtain a single enhanced signal out of the multiple channels. this paper investigates the use of mutual information for selecting the channel subset that produces the lowest error in a diarization system. conventional systems perform channel selection on the basis of signal properties such as snr, cross correlation. in this paper, we propose the use of a mutual information measure that is directly related to the objective function of the diarization system. the proposed algorithms are evaluated on the nist rt 06 eval dataset. channel selection improves the speaker error by 1.1% absolute (6.5% relative) w.r.t. the use of all channels.