waashk/dblp huggingface dataset:
parsimonious dictionary learning. sparse modeling of signals has recently received a lot of attention. often, a linear under-determined generative model for the signals of interest is proposed and a sparsity constraint imposed on the representation. when the generative model is not given, choosing an appropriate generative model is important, so that the given class of signals has approximate sparse representations. in this paper we introduce a new scheme for dictionary learning and impose an additional constraint to reduce the dictionary size. small dictionaries are desired for coding applications and more likely to “work” with suboptimal algorithms such as basis pursuit. another benefit of small dictionaries is their faster implementation, e.g. a reduced number of multiplication/addition in each matrix vector multiplication, which is the bottleneck in sparse approximation algorithms.
latent dirichlet learning for document summarization. automatic summarization is developed to extract the representative contents or sentences from a large corpus of documents. this paper presents a new hierarchical representation of words, sentences and documents in a corpus, and infers the dirichlet distributions for latent topics and latent themes in word level and sentence level, respectively. the sentence-based latent dirichlet allocation (slda) is accordingly established for document summarization. different from the vector space summarization, slda is built to fit the fine structure of text documents, and is specifically designed for sentence selection. slda acts as a sentence mixture model with a mixture of dirichlet themes, which are used to generate the latent topics in observed words. the theme model is inherent to distinguish sentences in a summarization system. in the experiments, the proposed slda outperforms other methods for document summarization in terms of precision, recall and f-measure.
improved subspace doa estimation methods with large arrays: the deterministic signals case. this paper is devoted to the subspace doa estimation using a large antennas array when the number of available snapshots is of the same order of magnitude than the number of sensors. in this context, the traditional subspace methods fail because the empirical covariance matrix of the observations is a poor estimate of the true covariance matrix. mestre et al. proposed recently to study the behaviour of the traditional estimators when the number of antennas m and the number of snapshots n converge to +∞ at the same rate. using large random matrix theory results, they showed that the traditional subspace estimate is not consistent in the above asymptotic regime and they proposed a new consistent subspace estimate which outperforms the standard subspace method for realistic values of m and n. however, the work of mestre et al. assumes that the source signals are independent and identically distributed in the time domain. the goal of the present paper is to propose new consistent estimators of the doas in the case where the source signals are modelled as unknown deterministic signals. this, in practice, allows to use the proposed approach whatever the statistical properties of the source signals are.
differential feedback of mimo channel correlation matrices based on geodesic curves. this paper proposes a differential quantization strategy to be used in the feedback link of a multi-input-multi-output (mimo) communication system. this algorithm is applied to the channel correlation matrix exploiting geodesic curves and the intrinsic geometry of positive definite hermitian matrices. simulation results in the paper show that the proposed algorithm improves other techniques based on the direct quantization of the channel response matrix or the quantization of the subspace spanned by the strongest eigenmodes of the mimo channel, i.e., grassmannian based techniques. the main drawback of grassmananian based algorithms is that the transmitter is constrained to apply a uniform power allocation among eigenmodes, which is not forced in the algorithm proposed in this paper.
compression of image patches for local feature extraction. local features are widely used for content-based image retrieval and object recognition. we present an efficient method for encoding digital images suitable for local feature extraction. first, we find the patches in the image corresponding to the detected features. then, we extract these patches at their characteristic scale and orientation and encode them for efficient transmission. a discrete cosine transform (dct) with adaptive block size is used for patch compression. we compare this method to directly compressing feature descriptors using transform coding. experimental results show the superior performance of our technique. image patches can be compressed to rates around 55 bits/patch (18x compression relative to uncompressed sift feature descriptors) and still achieve good image matching performance.
high-rate distributed multi-source cooperation using complex field coding. a multisource cooperative protocol is developed capable of achieving diversity order up to the number of cooperating users at a high throughput. in this design each source jointly encodes its own new information symbol with the information symbols received from other sources at past instants. joint encoding is done using linear complex-field coefficients. throughput analysis shows gains with respect to existing multi-source protocols and approaches the throughput of non-cooperative schemes. diversity analysis shows that full spatial diversity is achievable. simulations confirm the analytically established assessments.
i/q imbalance mitigation for time-reversal stbc systems over frequency-selective fading channels. this work studies the effect of in-phase/quadrature (i/q) imbalance on time-reversal space-time block coded (tr-stbc) communication systems operating over frequency-selective fading channels. the transceiver i/q imbalance in a 2 × 1 tr orthogonal stbc (tr-ostbc) system is studied in detail, and low-complexity mitigation solutions are proposed by exploiting the special structure of the received data. our results show that the proposed solutions both in time domain and in frequency domain can effectively mitigate the i/q distortion in such a system.
joint estimation of i/q imbalance, cfo and channel response for ofdm systems. in this paper, we study the joint estimation of i/q imbalance, cfo and channel response for ofdm systems. using two repeated ofdm blocks for training, we propose a new method to solve the joint estimation problem. the implementation complexity is low. simulation results show that the proposed method has a good performance and its ber is very close to the ideal case where all the parameters are known perfectly at the receiver.
robust 2-d channel estimation for multi-carrier systems with finite dimensional pilot grid. pilot-aided channel estimation for multi-carrier systems can be significantly improved by exploiting time and frequency correlations between the channel frequency response coefficients. but in practice, the knowledge of channel correlation function is not accurately available, thereby necessitating the need of an estimator that employs a fixed correlation function and is robust to mismatches with the actual one. while the maximally robust channel estimator for multi-carrier systems for the case of an infinite number of observations is well known for almost a decade, the one for the case of a finite number of observations has been only recently proposed. we extend the proposed maximally robust estimator to the practical case of small finite dimensional pilot grids by taking into account the grid edge effects. this paves the way for application of the estimator to practical systems such as 3g lte. simulation results for an lte uplink system under different transmission scenarios demonstrate the superiority of the proposed maximally robust estimator over the heuristic one by as much as 1.35 db in terms of the coded ber.
decision-based median filter using k-nearest noise-free pixels. traditional median filter replaces each pixel in an image with the median value of their k-nearest pixels (commonly known as pixels in 2-d window). the problem associated with this approach is that the restored pixel is noise if median value of their k-nearest pixels is a corrupted pixel. to mitigate the above problem, this paper proposes a novel decision-based median filter that replaces each corrupted pixel with the median value of their k-nearest noise-free pixels. advantages of the median filter using k-nearest noise-free pixels instead of k-nearest pixels are two facets: first, it guarantees that pixels after being restored must be noise-free, because the median filter operator is executed on noise-free pixels; second, the median filter using k-nearest noise-free pixels adaptively adjusts its window size for each pixel such that the number of noise-free pixels locating in the window increases up to k. to realize it, the median filter using k-nearest noise-free pixels firstly detects noise-free pixels in an image, then replaces each corrupted pixel with the median value of their knearest noise-free pixels. the proposed median filter is tested on four real images corrupted by different levels of salt-and-pepper noise. experimental results confirm the effectiveness of decision-based median filter using k-nearest noise-free pixels.
didstributed source coding authentication of images with affine warping. media authentication is important in content delivery via untrusted intermediaries, such as peer-to-peer (p2p) file sharing. many differently encoded versions of a media file might exist. our previous work applied distributed source coding not only to distinguish the legitimate diversity of encoded images from tampering but also to localize tampered regions in an image already deemed to be inauthentic. in both cases, authentication requires a slepian-wolf encoded image projection that is supplied to the decoder. we extend our scheme to authenticate images that have undergone affine warping. our approach incorporates an expectation maximization algorithm into the slepian-wolf decoder. experimental results demonstrate that the proposed algorithm can distinguish legitimate encodings of authentic images from illegitimately modified versions, despite an arbitrary affine warping, using authentication data of less than 250 bytes per image.
automatic pronunciation error detection based on linguistic knowledge and pronunciation space. this paper presents a new approach that uses linguistic knowledge and pronunciation space for automatic detection of typical phone-level errors made by non-native speakers of mandarin. firstly, linguistic knowledge of common learner mistakes is embedded in the calculation of log-posterior probability and the revised log-posterior probability (rlpp) is regarded as the measure of mispronunciation; secondly, a restricted pronunciation space is constructed by using rlpp vectors to describe the characteristics of pronunciation and support vector machine (svm) classifier is applied into the detection of typical pronunciation errors. experiments based on a nonnative speaker database of mandarin confirm the promising effectiveness of our methods.
secure image retrieval through feature protection. this paper addresses the problem of image retrieval from an encrypted database, where data confidentiality is preserved both in the storage and retrieval process. the paper focuses on image feature protection techniques which enable similarity comparison among protected features. by utilizing both signal processing and cryptographic techniques, three schemes are investigated and compared, including bit-plane randomization, random projection, and randomized unary encoding. experimental results show that secure image retrieval can achieve comparable retrieval performance to conventional image retrieval techniques without revealing information about image content. this work enriches the area of secure information retrieval and can find applications in secure online services for images and videos.
a mimo-ofdm channel estimation scheme utilizing complementary sequences. we present a pilot-assisted method for estimating the frequency selective channel in a mimo-ofdm system. the pilot sequence is designed using the dft of the golay complementary sequences. novel exploitation of the perfect autocorrelation property of golay complementary sequences, in conjunction with ostbc based pilot waveform scheduling across multiple ofdm frames, facilitates simple separation of the channel mixtures at the receive antennas. the dft length used to transform the complementary sequence into the frequency domain is shown to be a key critical parameter for correctly estimating the channel. this channel estimation scheme is then extended to antenna arrays of arbitrary sizes.
fast acoustic computations using graphics processors. in this paper we present a fast method for computing acoustic likelihoods that makes use of a graphics processing unit (gpu). after enabling the gpu acceleration the main processor runtime dedicated to acoustic scoring tasks is reduced from the largest consumer to just a few percent even when using mixture models with a large number of gaussian components. the results show a large reduction in decoding time with no change in accuracy and we also show by using a 16bit half precision floating point format for the acoustic model parameters, storage requirements can be halved with no reduction in accuracy.
acoustic fall detection using gaussian mixture models and gmm supervectors. we present a system that detects human falls in the home environment, distinguishing them from competing noise, by using only the audio signal from a single far-field microphone. the proposed system models each fall or noise segment by means of a gaussian mixture model (gmm) supervector, whose euclidean distance measures the pairwise difference between audio segments. a support vector machine built on a kernel between gmm supervectors is employed to classify audio segments into falls and various types of noise. experiments on a dataset of human falls, collected as part of the netcarity project, show that the method improves fall classification f-score to 67% from 59% of a baseline gmm classifier. the approach also effectively addresses the more difficult fall detection problem, where audio segment boundaries are unknown. specifically, we employ it to reclassify confusable segments produced by a dynamic programming scheme based on traditional gmms. such post-processing improves a fall detection accuracy metric by 5% relative.
posterior features applied to speech recognition tasks with user-defined vocabulary. this paper presents a novel approach for those applications where vocabulary is defined by a set of acoustic samples. in this approach, the acoustic samples are used as reference templates in a template matching framework. the features used to describe the reference templates and the test utterances are estimates of phoneme posterior probabilities. these posteriors are obtained from a mlp trained on an auxiliary database. thus, the speech variability present in the features is reduced by applying the speech knowledge captured by the mlp on the auxiliary database. moreover, information theoretic dissimilarity measures can be used as local distances between features. when compared to state-of-the-art systems, this approach outperforms acoustic-based techniques and obtains comparable results to orthography-based methods. the proposed method can also be directly combined with other posterior-based hmm systems. this combination successfully exploits the complementarity between templates and parametric models.
syllable nucleus durations estimation using linear regression based ensemble model. unlike conventional automatic continuous speech segmentation models that deal with each boundary time-mark individually, in this paper, we propose an interval-data-based linear regression model for syllable nucleus durations estimation (lrm-de), which treats syllable boundary time-marks in pairs. this characteristic of lrm-de makes it more suitable for estimating syllable durations for english sentences, which can be used for sentence stress detection. lrm-de combines the outcomes of multiple base automatic speech segmentation machines (asms) to generate final boundary time-marks that miminize the average distance of the predicted and reference boundary-pairs of syllable nuclei. experimental results show that on timit dataset, lrm-de reduces the average difference between the predicted syllable nucleus durations and their reference ones from 13.64ms (the best result of a single asm) to 11.81ms. also, lrm-de improves the syllable nucleus segmentation accuracy from 81.59% to 83.98% within a tolerance of 20ms.
phase diffusion for the synchronization of heterogenous sensor streams. the analysis of complex human activity typically requires multiple sensors: cameras that take videos from different directions and in different areas, microphones, proximity sensors, range finders, and more. scenarios where it is not possible to associate reliable clocks to each of the sensors pose a synchronization problem between heterogeneous data streams. in this paper, we propose a new theoretical framework for measuring the synchrony between heterogenous sensor streams. the main idea is to model the phase disparity between two data streams explicitly as an ornstein-uhlenbeck random process. based on this model, we derive a simple method for synchronizing of underlying sources. we illustrate the ideas with experiments on audio-visual synchronization and human motion categorization, and report promising results.
face recognition with enhanced privacy protection. this paper presents a novel approach for face based biometric recognition. the proposed method is based on the sorted index numbers (sin) of appearance based facial features. a new algorithm is introduced to measure the similarity between sin vectors. due to the non-invertibility of the transformation from the original features to the sin vectors, the proposed method can preserve the privacy of the users. the effectiveness of the proposed method is tested on a large generic data set, which contains images from several well known face databases. experimental results demonstrate that the proposed solution may improve the recognition accuracy in both identification and verification scenarios.
precoder design for mimo broadcast channels with power leakage constraints. we investigate the design of mimo linear precoders for multiuser broadcast channels under signal leakage constraints. this power leakage constraints reflect both an important security consideration and the need for interference suppression at receivers. defining a convex optimization problem, we propose two sequential semi-definite program algorithms. in principle, our algorithms exploit both spatial diversity and multiuser diversity to enhance the overall system performance. the resulting mimo precoded transmission demonstrates superior performance over time-division multiplexed multiuser transmission in conjunction with the traditional space-time transmission.
mmse estimation in a linear signal model with ellipsoidal constraints. the estimation of an unknown parameter vector in a gaussian linear model is studied in this paper. two different cases are analyzed: the parameter vector is assumed to lie either in or on a given ellipsoid. the best estimator in terms of the mean squared error is derived. the performance of this estimator is analyzed and compared with the ordinary least squares, the constrained least squares and the linear minimax approach.
phase based features for cognitive load measurement system. the current automatic cognitive load measurement system based on mfcc and prosodic features does not take into account phase based speech information. this paper aims to improve the performance of the baseline system by introducing phase based features into the system. the additional features proposed are group delay features, all-pole model based fm features and zero crossing count based fm features. decrease in performance is observed when phase based features are considered individually or when concatenated with baseline features. however, significant performance improvement is observed when group delay features are fused with baseline features using linear combination score level fusion.
generative model-based speaker clustering via mixture of von mises-fisher distributions. this paper proposes a generative model-based speaker clustering algorithm in the maximum a posteriori adapted gaussian mixture model (gmm) mean supervector space. the algorithm can be viewed as an extension of the standard expectation maximization algorithm for fitting a mixture model to the data, which iterates between two steps - a sample re-assignment step (e-step) and a model re-estimation step (m-step) - until it converges. the directional scattering patterns of gmm mean supervectors suggest that we employ a mixture of von mises-fisher distributions in the model re-estimation step. in the sample re-assignment step, four sample-to-mixture assignment strategies, namely soft, hard, stochastic, and deterministic annealing assignments, are used. our experiments on the gale mandarin dataset show that the use of a mixture of von mises-fisher distributions as the underlying model yields significantly higher speaker clustering accuracies than the use of a mixture of gaussian distributions. it is further shown that deterministic annealing assignment outperforms soft assignment, that soft assignment is comparable to stochastic assignment, and that both soft and stochastic assignments outperform hard assignment.
a generalized family of parameter estimation techniques. the extended baum-welch (ebw) transformations is one of a variety of techniques to estimate parameters of gaussian mixture models. in this paper, we provide a theoretical framework for general parameter estimation and show the relationship between these different techniques. we introduce a general family of model parameter updates that generalizes a baum-welch (bw) recursive process to an arbitrary objective function of gaussian mixture models, and show how other common parameter estimation techniques belong to this family of model update rules. furthermore, we formulate the construction of an even more general family of update rules that has any specified value as a gradient steepness which belongs to the family of ebw gradient steepness, measuring how much an initial model is moved to an estimated updated model.
image inpainting via sparse representation. this paper proposes a novel patch-wise image inpainting algorithm using the image signal sparse representation over a redundant dictionary, which merits in both capabilities to deal with large holes and to preserve image details while taking less risk. different from all existing works, we consider the problem of image inpainting from the view point of sequential incomplete signal recovery under the assumption that the every image patch admits a sparse representation over a redundant dictionary. to ensure the visually plausibility and consistency constraints between the filled hole and the surroundings, we propose to construct a redundant signal dictionary by directly sampling from the intact source region of current image. then we sequentially compute the sparse representation for each incomplete patch at the boundary of the hole and recover it until the whole hole is filled. experimental results show that this approach can efficiently fill in the hole with visually plausible information, and take less risk to introduce unwanted objects or artifacts.