waashk/dblp huggingface dataset:
sub-band implementation of the harmonic music algorithm. in this paper, we present a novel method for joint estimation of the order and fundamental frequency of a set of harmonically related sinusoids. this method uses a subband based approach to estimate the involved parameters using subspace techniques, and the resulting algorithm is termed frequency-selective harmonic music (f-hmusic). the performance of f-hmusic is evaluated and compared to both harmonic music (hmusic) and cramér-rao lower bound (crlb). especially, in a low signal-to-noise ratio (snr) with colored noise scenarios, where f-hmusic outperforms hmusic. f-hmusic is concluded to be more computationally efficient and more robust against colored noise than other subspace based fundamental frequency estimators.
map approach to learning sparse gaussian markov networks. recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse markov networks result into convex problems that can be solved optimally and efficiently. however, the accuracy of such methods can be very sensitive to the choice of regularization parameter, and optimal selection of this parameter remains an open problem. herein, we propose a maximum a posteriori probability (map) approach that investigates different priors on the regularization parameter and yields promising empirical results on both synthetic data and real-life application such as brain imaging data (fmri).
assessment of baroreflex control of heart rate during general anesthesia using a point process method. evaluation of baroreflex control of heart rate (hr) has important implications in clinical practice of anesthesia and postoperative care. in this paper, we present a point process method to assess the dynamic baroreflex gain using a closed-loop model of the cardiovascular system. specifically, the inverse gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by a linear or bilinear bivariate regression on the previous r-r intervals and blood pressure (bp) measures. the instantaneous baroreflex gain is estimated in the feedback loop with a point process filter, while the rr→bp feedforward frequency response is estimated by a kalman filter. in addition, the instantaneous cross-spectrum and cross-bispectrum (as well as their ratio) can also be estimated. all statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics during general anesthesia.
unsupervised equalization of lombard effect for speech recognition in noisy adverse environment. when exposed to environmental noise, speakers adjust their speech production to maintain intelligible communication. this phenomenon, called lombard effect (le), is known to considerably impact the performance of automatic speech recognition (asr) systems. in this study, novel frequency and cepstral domain equalizations that reduce the impact of le on asr are proposed. short-time spectra of le speech are transformed towards neutral asr models in a maximum likelihood fashion. dynamics of cepstral coefficients are normalized to a constant range using quantile estimations. the algorithms are incorporated in a recognizer employing a codebook of noisy acoustic models. in a recognition task on connected czech digits presented in various levels of background car noise, the resulting system provides an absolute reduction in word error rate (wer) on 10 db snr data of 8.7% and 37.7% for female neutral and le speech, and of 8.7% and 32.8% for male neutral and le speech when compared to the baseline system employing perceptual linear prediction (plp) coefficients and cepstral mean and variance normalization.
joint linear-circular stochastic models for texture classification. in this paper, we investigate both linear and circular stochastic models in the context of texture discrimination. these models aim at representing the magnitudes and orientations obtained by a complex wavelet decomposition, such as the steerable pyramid.the novelty consists in considering specific parametric models for circular data such as von mises and ψ- distributions to describe the distributions of orientations. particular attention is paid to the choice of a metric and to its adequation to the models. indexing experiments are conducted to quantitatively evaluate the performances of the proposed models and of the chosen matrices, i.e. the l1 and kullback-leibler distances.
nonmatrix closed-form expressions of the cramér-rao bounds for near-field localization parameters. near-field source localization problem by a passive antenna array makes the assumption that the time-varying sources are located near the antenna. in this situation, the far-field assumption (planar wavefront) is no longer valid and we have to consider a more complicated model parameterized by the bearing (as in the far-field case) and by the distance, named range, between the source and a reference sensor. we can find a plethora of estimation schemes in the literature but the ultimate performance has not been fully investigated. in this paper, we derive and analyze the cramér-rao bound (crb) for a single time-varying source. in this case, we obtain nonmatrix closed-form expressions. our approach has two advantages: (i) the computational cost for a large number of snapshots of a matrix-based crb can be high while our approach is cheap and (ii) some useful informations can be deduced from the behavior of the bound. in particular, we show that closer is the source from the array and/or higher is the carrier frequency, better is the estimation of the range.
using a pitch-synchronous residual codebook for hybrid hmm/frame selection speech synthesis. this paper proposes a method to improve the quality delivered by statistical parametric speech synthesizers. for this, we use a codebook of pitch-synchronous residual frames, so as to construct a more realistic source signal. first a limited codebook of typical excitations is built from some training database. during the synthesis part, hmms are used to generate filter and source coefficients. the latter coefficients contain both the pitch and a compact representation of target residual frames. the source signal is obtained by concatenating excitation frames picked up from the codebook, based on a selection criterion and taking target residual coefficients as input. subjective results show a relevant improvement compared to the basic technique.
random patch based video tracking via boosting the relative spaces. in this paper, we propose a new visual tracking method based on the recently popular tracking-as-classification idea. we concentrate on exploring the intra-class variance of the foreground target to construct and update a classification based tracker. in our approach, foreground target is represented by a set of model patches. different types of features are jointly used to represent those patches. individual weak learners are trained based on each model patch's relative space. adaboost framework is applied to choose those weak classifiers to combine a strong classifier as the tracker for next frame. moreover, with the new tracking result, the tracker is adjusted adaptively according to the change of scene to keep itself discriminative during the entire sequence. we demonstrate the effectiveness of our approach with comparison results on common video sequences.
facial age estimation by multilinear subspace analysis. automatic estimation of human facial age is an interesting yet challenging topic appearing in recent years. since different people might age in different ways, solving the problem of age estimation involves two semantic labels: identity and age. in this paper, aging face images are organized in a third-order tensor according to both identity and age. due to the difficulty in data collection, the aging pattern for each person in the training set is always incomplete. therefore, the tensor contains a large amount of missing values. through a series of multilinear subspace analysis algorithms operating on tensor with missing values, the aging pattern contained in the training aging images can be iteratively learned and be used to predict the age of a given test image. in the experiment, the proposed method not only outperforms the existing algorithms, but also exceeds the human ability in age estimation.
local and global convergence behavior of non-equidistant sampling series. in this paper we analyze the local and global convergence behavior of sampling series with non-equidistant sampling points for the paley-wiener space pwπ1 and sampling patterns that are made of the zeros of sine-type functions. it is proven that the sampling series are locally uniformly convergent if no oversampling is used and globally uniformly convergent if oversampling is used. furthermore, we show that oversampling is indeed necessary for global uniform convergence, because for every sampling pattern there exists a signal such that the peak value of the approximation error grows arbitrarily large if no oversampling is used. finally, we use these findings to obtain similar results for the mean-square convergence behavior of sampling series for bandlimited wide-sense stationary stochastic processes.
context-independent phoneme recognition using a k-nearest neighbour classification approach. in this paper we investigate a non-parametric classification of english phonemes in speaker-independent continuous speech. we employ the “voting” k-nearest neighbour (k-nn) classifier, a powerful technique in pattern recognition problems, along with a new representation of phonemes for the speech recognition task. we also exploit the idea behind “approximate” k-nn that results in a very fast way of computing the k approximate closest neighbours of each data point. comparing the recognition performance of the proposed method with the hmm-based recognizer of htk toolkit reveals that the k-nn-based recognizer outperforms its counterpart. in addition, incorporating the “approximate” nearest neighbour search instead of the “exact” one results in completing the training step much faster than the hmm-based system, and the testing step with a comparable computational time. we also reduced the amount of the training data by applying a pattern recognition technique, called “thinning” algorithm. the outcome was a considerable reduction in the k-nn search space and hence the execution time, and also a slight increase in the recognition performance.
on the impact of spatial correlation and precoder design on the performance of mimo systems with space-time coding. the symbol error performance of spatially correlated multi-antenna systems is analyzed herein. when the transmitter only has statistical channel information, the use of space-time block codes still permits spatial multiplexing and mitigation of fading. the statistical information can be used for precoding to optimize some quality measure. herein, we analyze the performance in terms of the symbol error rate (ser). it is shown analytically that spatial correlation at the receiver decreases the performance both without precoding and with an ser minimizing precoder. without precoding, correlation should also be avoided at the transmitter side, but with an ser minimizing precoder the performance is actually improved by increasing spatial correlation at the transmitter. the structure of the optimized precoder is analyzed and the asymptotic properties at high and low snrs are characterized and illustrated numerically.
subspace tracking of fast time-varying channels in precoded mimo-ofdm systems. this paper presents a blind subspace-based tracking scheme for precoded mimo-ofdm systems over rapidly time-varying wireless channels. subspace-based tracking is normally considered for slow time-varying channels only. thanks to the frequency correlation of the wireless channels, the proposed scheme is able to collect data not only from the time but also from the frequency domain to speed up the update of the required second-order statistics. after each update of the statistics, the subspace information is also updated using orthogonal iteration, and then a new channel estimate is computed. the proposed algorithm is verified in 3gpp-scm suburban macro scenario, in which a mobile station is allowed to move in any direction with a constant speed of 100km/h. the simulation results show that the root mean square error of the channel estimates converges to the level of 2 × 10−2 within less than 5 ofdm symbols even for such a high doppler rate.
robust downlink beamforming using covariance channel state information. the problem of multiuser downlink beamforming is studied under the assumption that the transmitter has erroneous covariance-based channel state information (csi). the goal is to minimize the transmit power under the worst-case quality-of-service (qos) constraints. previous convex optimization-based solutions to this problem involve several coarse approximations of the original problem. in our proposed solution, such coarse approximations are avoided and an exact representation of the worst-case solution is obtained using lagrange duality. the so-obtained problem is then converted to a convex form using semidefinite relaxation (sdr). computer simulations show that the sdr step does not involve any approximation as the resulting solution is always rank-one. simulation results demonstrate substantial performance improvements over earlier worst-case optimization-based downlink beamforming techniques.
novel schemes for nonlinear acoustic echo cancellation based on filter combinations. nonlinear acoustic echo cancellers (nlaec) are becoming increasingly important in hands-free applications. however, in some situations, an nlaec is inferior to a linear aec, especially when the channel generates a negligible (or no) nonlinear echo. in general, the ratio of the linear to nonlinear echo signal power is unknown a priori, and will vary over time, thus making it difficult to know if an nlaec would improve or degrade the cancellation. in this paper, we present two novel solutions to this problem based on the adaptive combination of linear and nonlinear echo cancellers. both solutions perform efficiently regardless of the level of nonlinear echo. the benefits and robustness of both schemes are illustrated by experiments using laplacian colored noise and speech input signals.
power-aware content-adaptive h.264 video encoding. h.264 is a computationally intensive video codec striving for achieving the best quality for the compressed video. the computational complexity poses as a challenge for power-constrained applications. we present a system level complexity reduction for h.264 video encoding by allocating resources based on computational complexity and quality trade-off. we develop a framework which allocates the computational power of the encoder adaptive to video contents and also scales with the available battery power using a roi classification method. analysis is done to profile the key modules of the encoder which can be power-optimized while allocating resources. the results of the encoder module analysis are combined with the motion content analysis to obtain a power efficient encoder parameter set which reduces the computations and hence the power consumed. our simulation results on the jm h.264 framework confirm our hypothesis and computational savings of more than 50% with quality degradation less than 1% is achieved thereby extending it's feasibility for battery powered wireless devices.
strategies for modeling reverberant speech in the feature domain. the length of the room impulse response characterizing the acoustic path between speaker and microphone is significantly larger than the length of the analysis window used for feature extraction in automatic speech recognition (asr) systems. therefore, reverberation caused by multi-path propagation of sound waves from the speaker to distant-talking microphones has a dispersive effect on speech feature sequences. this dispersive effect causes a mismatch between the input speech and the acoustic models of the recognizer, usually trained on clean speech, and leads to a significant reduction of recognition performance. in this contribution, different strategies for obtaining acoustic models capturing the dispersive effect of reverberation are investigated in terms of modeling accuracy, flexibility with respect to changing reverberation conditions, effort for obtaining the reverberation representation and decoding complexity.
motion effect modeling in multipath configuration using warping based lag-doppler filtering. the estimation of the impulse response (ir) of a propagation channel is necessary for a large number of acoustic applications: underwater communication, detection and localization, etc. basically, it informs us about the distortions of a transmitted signal in one channel. this operation is usually subject to additional distortions due to the motion of the transmitter-channel-receiver configuration. this paper points on the effects of the motion while estimating the ir by matching filtering between the transmitted and the received signals and introduces a new motion compensation method. knowing the transmitted signal, the “apparent” speed of each propagation path can be estimated using wideband ambiguity function [1]. indeed, some interference appears in the wideband ambiguity plane because of the multipath propagation. a warping-based lag-doppler filtering method is proposed allowing us to accurately estimate the ir of the channel.
data-driven online variational filtering in wireless sensor networks. in this paper, a data-driven extension of the variational algorithm is proposed. based on a few selected sensors, target tracking is performed distributively without any information about the observation model. tracking under such conditions is possible if one exploits the information collected from extra inter-sensor rssi measurements. the target tracking problem is formulated as a kernel matrix completion problem. a probabilistic kernel regression is then proposed that yields a gaussian likelihood function. the likelihood is used to derive an efficient and accelerated version of the variational filter without resorting to monte carlo integration. the proposed data-driven algorithm is, by construction, robust to observation model deviations and adapted to non-stationary environments.
evaluation of a fused fm and cepstral-based speaker recognition system on the nist 2008 sre. in this paper, the fusion of two speaker recognition subsystems, one based on frequency modulation (fm) and another on mfcc features, is reported. the motivation for their fusion was to improve the recognition accuracy across different types of channel variations, since the two features are believed to contain complementary information. it was found that the mfcc-based subsystem outperformed the fm-based subsystem on telephone conversations from nist sre-06 dataset, while the opposite was true for nist sre-08 telephone data. as a result, the fm-based subsystem performed as well as the mfcc-based subsystem and their fusion gave up to 23% relative improvement in terms of eer over the mfcc subsystem alone, when evaluated on the nist 2008 core condition.
gain allocation in proportionate-type nlms algorithms for fast decay of output error at all times. in this paper, we propose three new proportionate-type nlms algorithms: the water filling algorithm, the feasible water filling algorithm, and the adaptive μ-law proportionate nlms (mpnlms) algorithm. the water filling algorithm attempts to choose the optimal gains at each time step. the optimal gains are found by minimizing the mean square error (mse) at each time with respect to the gains, given the previous mean square weight deviations. while this algorithm offers superior convergence times, it is not feasible. the second algorithm is a feasible version of the water filling algorithm. the adaptive mpnlms (ampnlms) algorithm is a modification of the mpnlms algorithm. in the mpnlms algorithm, the parameter μ of the μ-law compression is constant. in the ampnlms algorithm the parameter μ is allowed to vary with time. this modification allows the algorithm more flexibility when attempting to minimize the mse. compared with several feasible algorithms, the ampnlms algorithm has the fastest mse decay for almost all times.
modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle. using high throughput dna binding data for transcription factors and dna microarray time course data, we constructed four transcription regulatory networks and analysed them using a novel extension to the network component analysis (nca) approach. we incorporated probe level uncertainties in gene expression measurements into the nca analysis by the application of probabilistic principal component analysis (ppca), and applied the method to data from yeast metabolic cycle. analysis shows statistically significant enhancement to periodicity in a large fraction of the transcription factor activities inferred from the model. for several of these we found literature evidence of post-transcriptional regulation. accounting for probe level uncertainty of microarray measurements leads to improved network component analysis. transcription factor profiles showing greater periodicity at their activity levels, rather than at the corresponding mrna levels, for over half the regulators in the networks points to extensive post-transcriptional regulations.
field lines and players detection and recognition in soccer video. objects like field lines and players are important for semantic analysis in soccer video. in this paper, we propose effective methods for field lines and players detection and recognition. regions of field lines and players are first segmented from shot of wide angle view. gray value top-hat transform is then performed on the segmented region to detect field lines. mid-lines, end-lines and penalty-lines are then recognized based on their positions. template matching method is adopted to detect and recognize players. four types of templates for players are matched on the segmented region. accurate position of player can be obtained after template matching, and the type of player is also identified. experiment results on various data-sets with different production styles show effectiveness of our method.
minimum subspace noise tracking for noise power spectral density estimation. speech enhancement is the processing of speech signals in order to improve one or more perceptual aspects. if the statistics of the clean signal and the noise process are explicitly known, enhancement could be ‘optimally’ accomplished (minimizing a distortion measure between the clean and the estimated signals). in practice however, these statistics are not explicitly available, and the overall enhancement accuracy critically depends on the estimation quality of the unknown statistics. the estimation of noise (and speech) statistics is particularly a critical issue and a challenging problem under non-stationary noise conditions. in this paper, we investigate the noise floor estimation using subspace decomposition. we examine the speech dft rank limited assumption. we propose a new noise psd estimation scheme (called minimum subspace noise tracking (msnt)). the proposed scheme can be interpreted as a combination of the subspace structure and the minimum statistics tracking. experimental investigation of the msnt tracking performance and comparison with the state of the art is also presented.