waashk/dblp huggingface dataset:
joint source decoding in large scale sensor networks using markov random field models. scalable joint decoding of correlated observations transmitted using distributed quantization in a sensor-network is considered. in particular, quantized observations are modeled as a markov-random filed (mrf), from which we construct a factor-graph for implementing the decoder using the well known sum-product algorithm. an attractive property of this approach is that the decoder complexity can be controlled by the choice of the clique structure used to define the gibbs distribution of the mrf model. the experimental results obtained with a widely used correlated gaussian observation model is presented, which demonstrate that substantial performance gains can be achieved by joint decoding based on simple clique structures and potential functions.
incorporating monolingual corpora into bilingual latent semantic analysis for crosslingual lm adaptation. the major limitation in bilingual latent semantic analysis (blsa) is the requirement of parallel training corpora. motivated by semi-supervised learning, we propose a clusterbased blsa training approach to incorporate monolingual corpora. treating each parallel document pair as centroids of the parallel document clusters, each monolingual document is associated to the closest centroid according to their topic similarity. the resulting parallel document clusters are used as constraints to enforce a one-to-one topic correspondence in variational em. slight performance improvement in crosslingual language model adaptation is observed compared to the baseline without monolingual corpora.
complete characterization of perfectly secure stego-systems with mutually independent embedding operation. without any assumption on the cover source, this paper presents a complete characterization of all perfectly secure stego-systems that employ mutually independent embedding operation. it is shown that for a fixed embedding operation, the only perfectly secure stego-systems are those whose cover distribution is an element of a linear vector space with basis vectors determined by the embedding operation. moreover, we also prove that such stego-systems are perfectly secure if and only if the fisher information with respect to the embedding change rate is zero and thus fisher information can be seen as an equivalent descriptor of steganographic security. this result is important for deriving steganographic capacity of imperfect stego-systems with covers modeled as markov chains [1]. it also suggests that fisher information could be used for benchmarking.
optimal cepstrum estimation using multiple windows. the aim of this paper is to find a multiple window estimator that is mean square error optimal for cepstrum estimation. the estimator is compared with some known multiple window methods as well as with the parametric ar-estimator. the results show that the new estimator has high performance, especially for data with large spectral dynamics, and that it is also robust against parameter choices. simulated speech data is used for the evaluation. it is also shown that the windows of the estimator can be approximated with the sinusoidal multiple windows and that the weighting factors of the different periodograms can be analytically computed.
real-time stereo matching: a cross-based local approach. we propose an area-based local stereo matching algorithm that yields accurate disparity estimates, while achieving the real-time speed completely on the graphics processing unit (gpu). for a local stereo method, the key challenge is to decide an appropriate support window for the pixel under consideration. our stereo method starts with computing an upright local cross adaptively for each anchor pixel, which defines a per-pixel support skeleton. next, based on this compact local cross representation, we aggregate the matching costs in a shape adaptive full support region using two orthogonal integration steps. approximating scene structures accurately, the proposed method is among the best-performing real-time stereo methods according to the benchmark middlebury stereo evaluation. additionally, our method is very easy to implement, memory efficient, and hence it is promising for many practical applications.
an efficient hardware design of an optimal nonstationary filtering system. the development of a multi-cycle hardware design of a time-varying (tv) filtering system, suitable for real-time implementation on an integrated chip is outlined in this work. based on results of time-frequency (tf) analysis and the instantaneous frequency (if) estimation, the proposed design enables multiple detection of the local filter's region of support (frs) in the observed time-instant, resulting in the efficient filtering of multicomponent fm signals. the proposed design optimizes critical design performances (such as hardware complexity, energy consumption and hardware cost), making it a suitable system for real-time implementation on a chip. the design has been verified by an fpga (field-programmable gate array) circuit design.
applying improved spectral modeling for high quality voice conversion. in this work, accurate spectral envelope estimation is applied to voice conversion in order to achieve high-quality timbre conversion. true-envelope based estimators allow model order selection leading to an adaptation of the spectral features to the characteristics of the speaker. optimal residual signals can also be computed following a local adaptation of the model order in terms of the f0. a new perceptual criteria is proposed to measure the impact of the spectral conversion error. the proposed envelope models show improved spectral conversion performance as well as increased converted-speech quality when compared to linear prediction.
consensus-tracking in distributed networks by one-hop averaging. for a connected network of sensors we consider deriving the linear update weights required by a 1-hop distributed linear averaging algorithm (denoted 1-dla) such that average-consensus is reached when the sensor nodes simultaneously track, by linear stochastic approximation, a set of distinct markov chains with time-varying regime. it is found the desired consensus is infeasible for any 1-hop 1-dla type algorithm in this setting, which includes the consensus filter proposed in [2]. however, assuming a symmetric communication graph we show the average-consensus can be approached with zero asymptotic error by an alternative 1-hop algorithm (denoted 4-dla) that requires each sensor compute 4 estimates {π̂ s, s0, ŝ} rather than only {s} as required under 1-dla. we demonstrate a simulation of 4-dla and explain its advantages compared to alternative multi-hop algorithms.
automatic parameter optimization for a perceptual audio codec. in audio codec design, often various parameters have to be fixed which may have a dramatic impact on codec performance. in this paper, we report on successful optimization of a codec based on perceptual criteria. specifically, the peaq measure is used to determine the audio quality over a set of test items and search algorithms are used for optimization. first, simulated annealing is used for global search, then rosenbrock's method is used to further refine the result. as shown by an example, the improvements gained by optimization compared to an educated guess are substantial.
voiced/unvoiced pattern-based duration modeling for language identification. most existing duration modeling approaches facilitates phone recognizer and require manually annotated corpus to train the segmentation models, which is usually cost- and time-consuming. in this paper, a novel duration modeling approach is proposed, which does not require phone recognizer/annotated training data, and facilitates fast computation of language identification. in this approach, the segmentation is implemented by using articulatory features like voicing status. a pair of connected unvoiced and voiced segments is considered as the unit, and the duration of each segment is normalized for each utterance and then quantized into 20 discrete ranges. the ranges of units are later considered as symbol sequences and are modeled by n-gram models, to capture the temporal pattern, which is hypothesized to vary in different languages. the experiments based on the nist lre 2005 tasks show a relative 19.7% eer improvement by introducing the proposed duration modeling-based system into a fusion system containing two gmm-ubm based acoustic systems using mfcc and pitch+intensity features.
multi-resolution based hybrid spatiotemporal compression of encrypted videos. compression of encrypted data can be viewed as a special case of distributed source coding and can be achieved by applying slepian-wolf coding. however, how to compress the encrypted video efficiently remains a challenging problem especially for those videos with irregular high motion. this paper proposes a novel multi-resolution based approach which makes it possible not only to effectively derive the temporal side information from previous frames, but also to generate the spatial side information by having partial access to the current frame. the spatial and temporal side information can then be integrated adaptively to facilitate the compression. simulation results show that the proposed scheme significantly outperforms other schemes, especially for those video clips with irregular high motion.
part-of-speech histograms for genre classification of text. this work addresses the problem of classifying the genre of text, which is useful for a variety of language processing problems. we propose statistics of pos histograms as classification features, coupled with a quadratic discriminant classifier. in experiments on six different text and speech genres, we demonstrate enhanced performance compared to standard techniques using word frequency count features and pos trigram features. experiments on genres that were not seen in training show intuitive overlaps with the training classes.
ofdm symbol detection with interspersed pilot symbols and channel distribution information at the receiver. detection of ofdm transmissions with interspersed pilot symbols is considered. a hard output detection algorithm developed by taricco et. al. for flat fading channels is extended for this frequency selective scenario. in the investigated systems with 128 subcarriers, this algorithm outperforms conventional approaches and performs close to a genie aided receiver even with the use of a single pilot symbol for the whole frame. furthermore, a novel soft output generating algorithm is developed, which is more suitable for channel coded systems. the resultant algorithm is capable of detecting coded ofdm transmissions with fewer pilot symbols than that required for noiseless channel identification.
context-dependent pronunciation modeling for iraqi asr. in this paper, we introduce a novel pronunciation modeling technique that in contrast to existing techniques uses word context information. this context-dependent pronunciation modeling is designed to overcome the challenges posed by absence of diacritics in transcripts for training acoustic models for arabic dialects. to demonstrate the efficacy of the proposed pronunciation modeling, we present experimental results with both manually created and automatically generated vowelized lexicons on the darpa transtac colloquial iraqi corpus.
speech enhancement based on minima controlled recursive averaging incorporating conditional maximum a posteriori criterion. in this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (mcra) based on a conditional maximum a posteriori (map) criterion. from an investigation of the mcra scheme, it is discovered that mcra method cannot take full consideration of the inter-frame correlation of voice activity since the noise power estimate is adjusted by the speech presence probability depending on an observation of the current frame. to avoid this phenomenon, the proposed mcra approach incorporates the conditional map criterion in which the noise power estimate is obtained using the speech presence probability conditioned on both the current observation and the speech activity decision in the previous frame experimental results show that the proposed mcra technique based on conditional map yields better results compared to the conventional mcra method.
resource usage prediction for groups of dynamic image-processing tasks using markov modeling. with the introduction of dynamic image processing, such as in image analysis, the computational complexity has become data dependent and memory usage irregular. therefore, the possibility of runtime estimation of resource usage would be highly attractive and would enable quality-of-service (qos) control for dynamic image-processing applications with shared resources. a possible solution to this problem is to characterize the application execution using model descriptions of the resource usage. in this paper, we attempt to predict resource usage for groups of dynamic image-processing tasks based on markov-chain modeling. as a typical application, we explore a medical imaging application to enhance a wire mesh tube (stent) under x-ray fluoroscopy imaging during angioplasty. simulations show that markov modeling can be successfully applied to describe the resource usage function even if the flow graph dynamically switches between groups of tasks. for the evaluated sequences, an average prediction accuracy of 97% is reached with sporadic excursions of the prediction error up to 20–30%.
missing data recovery via a nonparametric iterative adaptive approach. we introduce a missing data recovery methodology based on a weighted least squares iterative adaptive approach (iaa). the proposed method is referred to as the missing-data iaa (miaa) and it can be used for uniform or non-uniform sampling as well as for arbitrary data missing patterns. miaa uses the iaa spectrum estimates to retrieve the missing data, based on a spectral least squares criterion similar to that used by iaa. numerical examples are presented to show the effectiveness of miaa for missing data recovery. we also show that miaa can outperform an existing competitive approach, and this at a much lower computational cost.
comparison of four estimators of the 3d cardiac electrical activity for surface ecg synthesis from intracardiac recordings. the aim of this study is to facilitate the home follow-up of patients treated with cardiac implantable devices. a new procedure to synthesize 12-lead ecg from intracardiac egm is proposed. it is based on the estimation of: (i) a 3d representation of the cardiac electrical activity both for ecg (vcg) and egm (vgm), and (ii) the transfer function between the vgm and the vcg. the extraction of vcg and vgmis performed by comparing four different algorithms based on pca and ica, whereas the non-linear transfer function between vcg and vgm is estimated using a specific neural network. results demonstrate the effectiveness of the proposed method in comparison with our previous work. indeed, the correlation coefficients, between the real ecg and the synthesized ecg, lie between 0.78 and 0.99, whereas correlation coefficients of the previous method (combining pca and linear wiener filter) lie between 0.6 and 0.94.
sparse probabilistic state mapping and its application to speech bandwidth expansion. in this paper we present a probabilistic algorithm that extracts a mapping between two subspaces by representing each subspace as a collection of states. an arbitrary increase in number of states results in over-fitting the training data without exploring the underlying structure of the map. this paper suggests a method to impose sparsity constraints on the state map by using entropic priors. this probabilistic model is applied to the problem of artificial bandwidth expansion that involves estimating the missing frequency components (3.7 – 8 khz and 0 – 0.3 khz) of speech given the narrowband speech signal (0.3 – 3.7 khz).
a practical walsh layering scheme for reliable transmission. concerning the uncertainty of channels and peak power constraint, we give a new practical layering scheme to do reliable transmission. in our scheme, walsh matrix is employed to do layer-time coding. regarding columns of a layer-time coding matrix as layers and rows as time, after walsh layer-time coding, interference among layers can be removed or diminished by adding rows up. when there are layers decoded successfully after the previous transmission, only not-yet-decoded layers will be retransmitted. simulation results show that our walsh layering scheme with hybrid automatic repeat request (harq) performs much better than the traditional single-layer arq sequential transmission with respect to average time delay.
anti-collusion fingerprinting with scalar costa scheme (scs) and colluder weight recovery. an anti-collusion fingerprinting system is developed to protect media files against time-varying collusion attacks based on the scalar costa scheme (scs) and colluder weight recovery. we treat the host signal as a parallel gaussian channel and fingerprints as transmitted user signals. we decompose the gaussian channel into multiple independent subchannels, and assign different user messages to different subchannels. then, colluder weights in collusion attacks can be estimated using pilot symbols at the decoder, and all weights can be estimated and compensated. as a result, the decoding region on the parametric space can be recovered as an original format. it is shown by experimental results that the proposed fingerprinting system has excellent performance in colluder detection.
a scalable method for voice search to nationwide business listings. voice search or 411-service is the task that finds a ranked set of directory listings that match a spoken query, where the target entries in the listing database and the spoken query may differ moderately in their syntactic form. while the conventional paradigm uses a two-box input (location + name), a single-box paradigm to voice search can allow users to provide all the information in a single utterance, thereby increasing query efficiency. furthermore, the scalability of traditional methods used in the two-box paradigm is infeasible, and alternative strategies that sacrifice accuracy are normally adopted. this work presents a scalable algorithm for directory search over a nationwide database of listings (millions of entries) without compromising recognition accuracy.
stereo-based stochastic mapping with discriminative training for noise robust speech recognition. this paper presents an enhanced stochastic mapping technique in the discriminative feature (fmpe) space that exploits stereo data for noise robust lvcsr. both mmse and map estimates of the mapping are given and the performance of the two is investigated. due to the iterative nature of the map estimate, we show that combining mmse and map estimates is possible and yields superior performance than each individual estimate. a multi-style discriminative training with minimum phone error (mpe) criterion is further applied to the compensated features and obtains significant performance improvement on real-world noisy test sets.
a reduced order model of head-related impulse responses based on independent spatial feature extraction. a reduced order model for large numbers of head-related impulse responses (hrirs) is proposed for real-time three-dimensional (3d) sound rendering. independent spatial features are firstly extracted from measured hrirs using independent component analysis (ica). these spatial feature vectors are not only mutually statistical independent but independent from all the measured azimuths. therefore filtering sound sources with numerous hrirs is transformed into filtering them using the extracted lower-dimensional feature vectors. furthermore balanced model truncation (bmt) method in a state space is adopted to reduce the order of each independent feature vector. simulation results demonstrate that our proposed algorithm not only acquires better approximated accuracy but has significantly lower computational complexity.
cross-validation of multiple language recognition systems using pseudo keys. in this paper, we present a pseudo-key analysis approach for cross-validation of language recognition systems before the ground truth (true key) becomes available. a state-of-the-art language recognition system typically employs multiple language recognition classifiers which are fused to form a mixture of experts. the individual classifiers are also called subsystems. to avoid the fused system from being brought down by some outlier classifiers, pseudo keys are designed to cross-examine the integrity of individual classifier candidates. the language recognition experiments are conducted on the nist 2007 language recognition evaluation (lre) corpus using the subsystems in the primary submission from the institute for infocomm research (iir).
time-sensitive behavior dynamics in multimedia fingerprinting social networks. multimedia social network is a network infrastructure in which the social network members share multimedia contents with all different purposes. analyzing user behavior in multimedia social networks help design more secured and efficient multimedia and networking systems. in this paper, we focus on the colluder social network in multimedia fingerprinting systems in which colluders gain reward by redistributing the colluded multimedia signal. however, the market value of the redistributed multimedia content is time sensitive. the earlier the colluded copy being released, the more the people who are willing to pay for it. thus the colluders have to reach agreement on how to distribute reward and the probability being detected among themselves as soon as possible. this paper incorporates the time-sensitiveness of the colluders' reward, and models the dynamics among colluders as a noncooperative game, and studies the time-restricted bargaining equilibrium. we provide the solution to the equilibrium that all the colluders have no inventive to disagree in order to maximize their own payoff.
blind system identification for speech dereverberation with forced spectral diversity. the common zeros problem for blind system identification (bsi) is well known. it degrades the performance of classic bsi algorithms and therefore imposes the limit on the performance of subsequent speech dereverberation. the effect of near-common zeros has recently been studied in terms of channel diversity and the degradation in performance of bsi and multichannel equalization algorithms has been shown. we now introduce a novel approach to improve channel diversity which we refer to as forced spectral diversity (fsd). the fsd concept uses a combination of spectral shaping filters and effective channel undermodelling. simulation results show that the proposed approach achieves improved performance with reduced complexity for multichannel bsi in a room acoustics example.
two microphone based direction of arrival estimation for multiple speech sources using spectral properties of speech. a two microphone direction of arrival (doa) estimation technique for multiple speech sources is developed which exploits speech specific properties, namely sparsity in time-frequency (spectrum) domain. for robustness, we exploit the sparsity in the frequency domain by focusing on the spectral content concentrated in sinusoidal tracks obtained through sinusoidal modeling. when multiple speeches are mixed in the two microphone system, the inter-channel phase differences (ipd) between the dual channels on those sinusoidal tracks will be dominated by the spatial information of the most powerful source at that specific time-frequency point because of the spectrum sparsity and masking effects. thereby, the source localization problem is turned into a clustering problem on the ipd versus frequency plot, and the generalized mixture decomposition algorithm (gmda) is used to cluster the groups of points corresponding to multiple sources. the doa of each source is derived from the parameters of each cluster. experimental results conducted show the scheme to be very effective.