waashk/dblp huggingface dataset:
a nonparametric test for stationarity based on local fourier analysis. in this paper we propose a nonparametric hypothesis test for stationarity based on local fourier analysis. we employ a test statistic that measures the variation of time-localized estimates of the power spectral density of an observed random process. for the case of a white gaussian noise process, we characterize the asymptotic distribution of this statistic under the null hypothesis of stationarity, and use it to directly set test thresholds corresponding to constant false alarm rates. for other cases, we introduce a simple procedure to simulate from the null distribution of interest. after validating the procedure on synthetic examples, we demonstrate one potential use for the test as a method of obtaining a signal-adaptive means of local fourier analysis and corresponding signal enhancement scheme.
detection from a multi-channel sensor using a hierarchical bayesian model. direct imaging of exoplanets involves very low signal-to-noise ratio data, that need to be carefully acquired and processed. multi-band devices enable the simultaneous record of images in different spectral bands. they can be used either for spectroscopy purposes or to improve detection capabilities.
approximating 3d shape using bezier surface. estimating 3d shape of the object is an important research topic in the area of computer vision, with a wide range of applications. this paper introduces a new method for focused-based passive methods (like sff) to approximate the 3d shape of the object using bezier surface. the discrete nature of image sampling results in the loss of information between two consecutive images. conventional approximation methods optimize or interpolate focus values. we have suggested interpolating depth values instead of focus value over a small patch on the object surface. the method approximates the surface more accurately and also reduces any noise caused by focus measure. the proposed method is tested and analyzed to demonstrate the effectiveness against traditional methods.
training-based bayesian mimo channel and channel norm estimation. training-based estimation of channel state information in multi-antenna systems is analyzed herein. closed-form expressions for the general bayesian minimum mean square error (mmse) estimators of the channel matrix and the squared channel norm are derived in a rayleigh fading environment with known statistics at the receiver side. when the second-order channel statistics are available also at the transmitter, this information can be exploited in the training sequence design to improve the performance. herein, mean square error (mse) minimizing training sequences are considered. the structure of the general solution is developed, with explicit expressions at high and low snrs and in the special case of uncorrelated receive antennas. the optimal length of the training sequence is equal or smaller than the number of transmit antennas.
extracting regions of attention by imitating the human visual system. detecting and segmenting out the regions of interest (rois) is one of the foundations in image processing and analysis. because the final information sink of images is human, for segmenting out the rois effectively, we need to study human visual system (hvs) and imitate the behaviors when human viewing a scene. researchers have found several factors which affect human attentions by studying eye movements when one views an image. in this paper, a method is proposed to detect the rois automatically based on hvs. in the proposed algorithm, the properties of pixels such as the contrast, location and edges are analyzed, and the pixels are enhanced according to the sensitivity of hvs. then these factors are combined to a salient map, which classifies each pixel of the image in relation to its perceptual importance. finally, the rois are segmented according to the salient map. this algorithm is easy to work, and can segment the objects from complex background efficiently.
improved design of two and four-group decodable stbcs with larger diversity product for eight transmit antennas. recently, full rate and full diversity two-group (2gp) and four-group (4gp) decodable space-time block codes (stbc) derived from quasi-orthogonal stbc (qstbc) and designed under diversity product maximization criterion have been proposed. in this paper, we derive an upper bound of diversity product for those stbcs and discover that the diversity product of the current 2gp-qstbc and 4gp-qstbc has the potential to approach the upper bound for 8 transmit antennas. to this end, we propose an improved design of 2gp and 4gp stbc with increased diversity product for 8 transmit antennas by allowing sufficient number of dimensions for constellation rotation. the diversity product of the proposed two-group decodable stbc achieves the derived upper bound.
real-time speech enhancement in noisy reverberant multi-talker environments based on a location-independent room acoustics model. this paper describes a new real-time speech enhancement method that reduces signal distortion caused by stationary noise and late reflections of reverberation in speech signals captured by a single distant microphone under multi-talker conditions. a major problem here is how to estimate the energy of the late reflections in real time when the room impulse responses from individual talkers to the microphone are not given or fixed in advance. to solve this problem, we introduce a probabilistic room acoustics model, and provide a method for estimating the energy of late reflections based on this model. in this method, parameters of the model for a room can be fixed in advance only from a few seconds of observation. by incorporating the proposed approach into a conventional frequency domain noise reduction scheme, we realize an integrated real-time speech enhancement framework. the effectiveness of the proposed method is confirmed experimentally for a case where there are two talkers in a room.
bounds on distributed tdoa-based localization of ofdm sources. one main drawback of using time difference of arrival (tdoa) methods for source localization and navigation is that they require centralization of multiple copies of a signal. this paper considers blindly estimating the location of a cyclic prefix (cp) in an orthogonal frequency division multiplexing (ofdm) signal, enabling distributed tdoa computation up to an integer ambiguity. this ambiguity can be resolved using integer least-squares methods, if enough tdoas are available. the contributions of this paper are derivation of the cramer-rao lower bound (crlb) on locating the cp, and hence on the underlying source localization problem.
sinr distribution for lte downlink multiuser mimo systems. the lte downlink multiuser multiple input multiple output (mimo) systems are analyzed in this paper. two spatial division multiplexing (sdm) multiuser mimo schemes are investigated: single user (su) and multi-user (mu) mimo schemes. the main contribution of this paper is the establishment of a mathematical model for the signal to interference plus noise ratio (sinr) distribution for multiuser sdm mimo systems with frequency domain packet scheduler.
robust mobile terminal tracking in nlos environments using interacting multiple model algorithm. an extended kalman filter-based interacting multiple model algorithm (imm-ekf) is proposed for mobile terminal tracking in cellular networks based on time of arrival estimates. the proposed imm-ekf is able to cope with line-of-sight (los) and non-line-of-sight (nlos) conditions modeled by a markov chain, where the los and nlos errors are described by different noise models. road-constraints are included into the imm-ekf to improve performance. simulation results show that the imm-ekf outperforms conventional methods. a comparison to the posterior cramér-rao lower bound is given to demonstrate the effectiveness of the imm-ekf.
document reconstruction using dynamic programming. in this work we propose a methodology for document reconstruction based on dynamic programming and a modified version of the prim's algorithm. firstly, we use polygonal approximation to reduce the complexity of the boundaries and extract features from them. thereafter, these features are used to feed the lcs dynamic programming algorithm. the scores yielded by the lcs algorithm are then used into a modified prim's algorithm to find the best match among all pieces. comprehensive experiments on a database composed of 100 shredded documents support the efficiency of the proposed methodology. when compared to global search algorithms, this approach brings an improvement of 18% in the number of fragments reconstructed.
estimating multiple transmitter locations from power measurements at multiple receivers. we consider the estimation of the locations of multiple transmitters based on received signal strength measurements at a network of randomly-placed receivers. we generalize the expectation-maximization (em) method to create a quasi em algorithm for localization under lognormal shadowing. simulated performance is compared to a state-of-the-art global optimizer and to random guessing. results reveal that the proposed quasi em algorithm outperforms both alternatives in median and ninety-fifth percentile error, especially as the number of receivers increases.
fingerprint matching based on distance metric learning. this paper considers a method for learning a distance metric in a fingerprinting system which identifies a query content by measuring the distance between its fingerprint and a fingerprint stored in a database. a metric having a general form of the mahalanobis distance is learned with the goal that the distance between fingerprints extracted from perceptually similar contents should be smaller than the distance between fingerprints extracted from perceptually dissimilar contents. the metric is learned by minimizing a cost function designed to achieve the goal. the cost function is convex, and the global minimum can be obtained using convex optimization. in our experiment, the distance metric learning is applied in an audio fingerprinting system, and it is experimentally shown that the learned distance metric improves the identification performance.
multi-dimensional space-time-frequency component analysis of event related eeg data using closed-form parafac. the efficient analysis of electroencephalographic (eeg) data is a long standing problem in neuroscience, which has regained new interest due to the possibilities of multidimensional signal processing. we analyze event related multi-channel eeg recordings on the basis of the time-varying spectrum for each channel. it is a common approach to use wavelet transformations for the time-frequency analysis (tfa) of the data. to identify the signal components we decompose the data into time-frequency-space atoms using parallel factor (parafac) analysis. in this paper we show that a tfa based on the wigner-ville distribution together with the recently developed closed-form parafac algorithm enhance the separability of the signal components. this renders it an attractive approach for processing eeg data. additionally, we introduce the new concept of component amplitudes, which resolve the scaling ambiguity in the parafac model and can be used to judge the relevance of the individual components.
geometric distortion signatures for printer identification. we present a forensic technique for analyzing a printed image in order to trace the originating printer. our method, which is applicable for commonly used electrophotographic (ep) printers, operates by exploiting the geometric distortion that these devices inevitably introduce in the printing process. in the proposed method, first a geometric distortion signature is estimated for an ep printer. this estimate is obtained using only the images printed on the printer and without access to the internal printer controls. once a database of printer signatures is available, the printer utilized to print a test image is identified by computing the geometric distortion signature from test image and correlating the computes signatures against the printer signatures in the database. experiments conducted over a corpus of ep printers demonstrate that the geometric distortion signatures of test documents exhibit high correlation with the corresponding printer signatures and a low correlation with other printer signatures. the method is therefore quite promising for forensic printer identification applications. we highlight several of the capabilities and challenges for the method.
real-time pda-based recursive fourier transform implementation for cochlear implant applications. this paper presents a real-time and interactive implementation of the recursive fourier transform approach on pda platforms for cochlear implant signal processing applications. pda platforms provide a cost-effective and portable platform for cochlear implant studies. the computational complexities of the two commonly used signal processing strategies that are being used in commercial cochlear implants are compared. a more computationally efficient approach using recursive fourier transform is discussed and a real-time implementation of this approach is then accomplished on a pda platform. different versions of the implementation are examined and compared in terms of speed and accuracy.
frequency invariant mvdr beamforming without filters and implementation using mimo radar. frequency invariant beamforming with sensor arrays is generally achieved using filters in the form of tapped delay-lines following each sensor. however it has been recently shown that with the help of the rectangular smart antenna array, it is possible to generate frequency invariant beampattern without using filters. in this paper, this frequency invariant beamforming technique is utilized to perform mvdr beamforming in the beamspace by designing frequency invariant beams spanning the desired range of azimuthal angles and optimally combining them. however the performance of the frequency invariant beamformer depends on the number of sensors which could be large for a rectangular array of size m × n. making use of the virtual array concept used in mimo radar, a novel method of producing the same frequency invariant beam, using only m transmitting and n receiving antennas, is proposed and a design example is provided to demonstrate the idea.
exploiting multiple shift invariances in harmonic retrieval. a novel algorithm for estimating multi-dimensional damped harmonics is proposed. a matrix polynomial is formed from the weighed sum of multiple shift invariances contained in the data model. necessary and sufficient conditions are derived that reveal that the damped harmonics can be uniquely determined from the roots of the matrix polynomial. the proposed algorithm reveal a seamless link between two important classes of search free subspace methods. the classical rooting based harmonic estimation methods that exploit the complete invariance structure and the single invariance esprit algorithms. we show that both approaches can be expressed under this general framework by an appropriate choice of the weights. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
does canonical correlation analysis provide reliable information on data correlation in array processing? this work provides analytical results on the canonical correlation analysis (cca) of data sets from two spatially separated arrays of sensors. our case studies cover both single source and multiple source signals in either white or colored noise fields for array signal processing. we derive analytical expressions of the canonical correlation for these examples and present a computer simulation analysis of empirical canonical correlations as a function of nominal correlation, signal-to-noise ratio (snr), and sample support. results obtained reveal an interesting fact that the canonical coefficients from cca provide reliable information on the spatial correlation existing among data sets from two arrays only when the snrs at both arrays are reasonably high. when sample correlation matrices (scm) are used in the empirical cca, reliable correlation can be estimated from cca asymptotically (either at high snrs from both arrays, or with a large number of snapshots in comparison with array dimensionality).
a speech fragment approach to localising multiple speakers in reverberant environments. sound source localisation cues are severely degraded when multiple acoustic sources are active in the presence of reverberation. we present a binaural system for localising simultaneous speakers which exploits the fact that in a speech mixture there exist spectro-temporal regions or ‘fragments’, where the energy is dominated by just one of the speakers. a fragment-level localisation model is proposed that integrates the localisation cues within a fragment using a weighted mean. the weights are based on local estimates of the degree of reverberation in a given spectro-temporal cell. the paper investigates different weight estimation approaches based variously on, i) an established model of the perceptual precedence effect; ii) a measure of interaural coherence between the left and right ear signals; iii) a data-driven approach trained in matched acoustic conditions. experiments with reverberant binaural data with two simultaneous speakers show appropriate weighting can improve frame-based localisation performance by up to 24%.
light-weight salient foreground detection with adaptive memory requirement. designing algorithms, which require less memory and consume less power, is very important for the portability to embedded smart cameras, which have limited resources. we present a light-weight and efficient algorithm for salient foreground detection that is highly robust against lighting variations and non-static backgrounds such as scenes with swaying trees. contrary to traditional methods, memory requirement for the data saved for each pixel is very small in the proposed algorithm. moreover, the total memory requirement is adaptive, and is decreased even more depending on the amount of activity in the scene. as opposed to existing methods, we treat each pixel differently based on its history. instead of requiring the same amount of memory for every pixel, we allocate less memory for stable background pixels. the plot of the required memory at each frame also serves as a tool to find the video portions with high activity.
stochastic modeling of vehicle trajectory during lane-changing. a signal processing approach for modeling vehicle trajectory during lane changing driving is discussed. because individual driving habits are not a deterministic process, we developed a stochastic method. the proposed model consists of two parts: a dynamic system represented by a hidden markov model and a cognitive distance space derived from the range distance distribution. the first part models the local dynamics of vehicular movements and generates a set of probable trajectories. the second part selects an optimal trajectory by stochastically evaluating the distances from surrounding vehicles. from experimental evaluation, we show that the model can predict the vehicle trajectory at given traffic conditions with 17.6 m prediction error for two different drivers.
co-adaptation: adaptive co-training for semi-supervised learning. inspired by popular co-training and domain adaptation methods, we propose a co-adaptation algorithm. the goal is improving the performance of a dialog act segmentation model by exploiting the vast amount of unlabeled data. this task provides a nice framework for multiview learning, as it has been shown that lexical and prosodic features provide complementary information. instead of simply adding machine-labeled data to the set of manually labeled data, co-adaptation technique adapts the existing models. while both co-training and domain adaptation techniques have been employed for dialog act segmentation, our experiments show that the proposed co-adaptation algorithm results in significantly better performance.
resampling auxiliary data for language model adaptation in machine translation for speech. performance of n-gram language models depends to a large extent on the amount of training text material available for building the models and the degree to which this text matches the domain of interest. the language modeling community is showing a growing interest in using large collections of auxiliary textual material to supplement sparse in-domain resources. one of the problems in using such auxiliary corpora is that they may differ significantly from the specific nature of the domain of interest. in this paper, we propose three different methods for adapting language models for a speech to speech (s2s) translation system when auxiliary corpora are of different genre and domain. the proposed methods are based on centroid similarity, n-gram ratios and resampled language models. we show how these methods can be used to select out of domain textual data such as newswire text to improve a s2s system. we were able to achieve an overall relative improvement of 3.8% in bleu score over a baseline system that uses only in-domain conversational data.
joint uncertainty decoding with the second order approximation for noise robust speech recognition. joint uncertainty decoding has recently achieved promising results by integrating the front-end uncertainty into the back-end in a mathematically consistent framework. in this paper, joint uncertainty decoding is compared with the widely used vector taylor series (vts). we show that the two methods are identical except that joint uncertainty decoding applies the taylor expansion on each regression class whereas vts applies it to each hmm mixture. the relatively rougher expansion points used in joint uncertainty decoding make it computationally cheaper than vts but inevitably worse on recognition accuracy. to overcome this drawback, this paper proposes an improved joint uncertainty decoding algorithm which employs second-order taylor expansion on each regression class in order to reduce the expansion errors. special considerations are further given to limit the overall computational cost by adopting different number of regression classes for different orders in the taylor expansion. experiments on the aurora 2 database show that the proposed method is able to beat vts on recognition accuracy and computational cost with relative improvement up to 6% and 60%, respectively.
joint watermarking and compression for gaussian and laplacian sources using uniform vector quantization. using fixed rate uniform vector quantization, in this paper, we consider how to design a joint watermarking and compression (jwc) system for gaussian and laplacian sources to maximize the robustness in the presence of additive gaussian attacks under constraints on the compression rate and quantization distortion. firstly, we construct vector quantizers shaped to match the multidimensional distribution of source signals. then we scale codebooks corresponding to the vector quantizers to maximize the robustness of the watermarks against the additive gaussian attacks. simulation results show that the proposed scheme can achieve up to 0.92 db distortion-to-noise ratio (dnr) gain over jwc schemes using uniform scalar quantization while maintaining the simplicity of implementation with uniform quantization.