waashk/dblp huggingface dataset:
ber improved transmit power allocation for d-sttd systems with qr-based successive symbol detection. we propose a ber improved power allocation scheme for d-sttd systems over i.i.d. rayleigh fading channels under the qr-based successive detection framework. instead of relying on ber under a fixed channel realization, the adopted design criterion is the mean ber (assuming there is no inter-layer error propagation) averaged with respect to the channel distribution. such a design metric has two-fold advantages: (i) it is analytically tractable and is closely related to a block error probability upper bound when inter-layer error propagation occurs, and (ii) there is no need for repeated feedback of the instantaneous channel information. by exploiting a distinctive channel matrix structure unique to d-sttd systems we derive a closed-form approximate upper bound of the considered ber metric; through minimization of this bound an optimal power allocation scheme is obtained. numerical simulation is used to illustrate the performance of the proposed method.
revisiting graphemes with increasing amounts of data. letter units, or graphemes, have been reported in the literature as a surprisingly effective substitute to the more traditional phoneme units, at least in languages that enjoy a strong correspondence between pronunciation and orthography. for english however, where letter symbols have less acoustic consistency, previously reported results fell short of systems using highly-tuned pronunciation lexicons. grapheme units simplify system design, but since graphemes map to a wider set of acoustic realizations than phonemes, we should expect grapheme-based acoustic models to require more training data to capture these variations. in this paper, we compare the rate of improvement of grapheme and phoneme systems trained with datasets ranging from 450 to 1200 hours of speech. we consider various grapheme unit configurations, including using letter-specific, onset, and coda units. we show that the grapheme systems improve faster and, depending on the lexicon, reach or surpass the phoneme baselines with the largest training set.
shrinkage estimation of high dimensional covariance matrices. we address covariance estimation under mean-squared loss in the gaussian setting. specifically, we consider shrinkage methods which are suitable for high dimensional problems with small number of samples (large p small n). first, we improve on the ledoit-wolf (lw) method by conditioning on a sufficient statistic via the rao-blackwell theorem, obtaining a new estimator rblw whose mean-squared error dominates the lw under gaussian model. second, to further reduce the estimation error, we propose an iterative approach which approximates the clairvoyant shrinkage estimator. convergence of this iterative method is proven and a closed form expression for the limit is determined, which is called the oas estimator. both of the proposed estimators have simple expressions and are easy to compute. although the two methods are developed from different approaches, their structure is identical up to specific constants. the rblw estimator provably dominates the lw method; and numerical simulations demonstrate that the oas estimator performs even better, especially when n is much less than p.
spoken term detection using fast phonetic decoding. while spoken term detection (std) systems based on word indices provide good accuracy, there are several practical applications where it is infeasible or too costly to employ an lvcsr engine. an std system is presented, which is designed to incorporate a fast phonetic decoding front-end and be robust to decoding errors whilst still allowing for rapid search speeds. this goal is achieved through monophone open-loop decoding coupled with fast hierarchical phone lattice search. results demonstrate that an std system that is designed with the constraint of a fast and simple phonetic decoding front-end requires a compromise to be made between search speed and search accuracy.
synthesis of planar arrays with arbitrary geometry for flat-top footprint patterns. this paper presents a new synthesis algorithm to produce flat-top main beams with arbitrary footprint for array elements placed in an arbitrary planar geometry. the general framework of the paper would encompass the patterns produced by circular aperture distribution. it is shown that for patterns generated by circular apertures some efficient simplifying facts based on properties of bessel functions can be applied. the method shows a high performance in generating optimally flat-top patterns with detailed geometry footprints.
a sum-of-products model for effective coherent modulation filtering. modulation filtering is a technique for filtering slowly-varying envelopes of frequency subbands of a nonstationary signal, ideally without affecting the signal's phase and fine-structure. coherent modulation filtering is a promising subtype of such techniques where subband envelopes are determined through demodulation of the subband signal with a coherently detected subband carrier. in this paper we demonstrate how modulation filtering, when done coherently, is far more effective than standard incoherent methods. we show that empirical results can be made to be almost ideal, and significantly better than previous coherent attempts, as long as fine-structure information is retained as side information and the filterbank reduces subband interference.
loquendo - politecnico di torino's 2008 nist speaker recognition evaluation system. this paper describes the improvements introduced in the loquendo-politecnico di torino (lpt) speaker recognition system submitted to the nist sre08 evaluation campaign. this system, which was among the best participants in this evaluation, combines the results of three core acoustic systems, two based on gaussian mixture models (gmms), and one on phonetic gmms. we discuss the results of the experiments performed for the 10sec-10sec condition and for the core condition, including the challenging tasks involving a target speaker and an interviewer. the error rate reduction of our sre08 system compared to the sre06 system ranges from 25% of the telephone-interview condition to 57% of the interview-interview condition. on the test with telephone and microphone conversations, the improvements range from 9% to 32%.
musical noise analysis based on higher order statistics for microphone array and nonlinear signal processing. in this paper, we conduct an analysis for reduction of musical noise in integration method of microphone array signal processing and nonlinear signal processing. in these days, for better noise reduction, integration methods of microphone array signal processing and nonlinear signal processing have been researched. however, non-linear signal processing causes musical noise. since such musical noise make users uncomfortable, it is desired that musical noise is mitigated. moreover, in these days, it is reported that higher-order statistics is strongly related with the amount of generated musical noise. thus, we analyze the integrated method of microphone array signal processing and nonlinear signal processing, based on higher-order statistics. also, we propose an architecture for reducing musical noise based on the analysis. the effectiveness of the proposed architecture and analysis correctness are shown via a computer simulation and a subjective evaluation.
modelling the neurovascular habituation effect on fmri time series. in this paper, a novel non-stationary model of functional magnetic resonance imaging (fmri) time series is proposed. it allows us to account for some putative habituation effect arising in event-related fmri paradigms that involves the so-called repetition-suppression phenomenon [1] and induces decreasing magnitude responses over successive trials. akin to [2], this model is defined over functionnally homogeneous regions-of-interest (rois) and embedded in a joint detection-estimation approach of brain activity. importantly, its non-stationarity character is embodied in the trial-varying nature of the bold response magnitude. habituation and activation maps are then estimated within the bayesian framework in a fully unsupervised mcmc procedure. on artificial fmri datasets, we show that habituation effects can be accurately recovered in activating voxels.
combination strategies for a factor analysis phone-conditioned speaker verification system. this work aims to take advantage of recent developments in joint factor analysis (jfa) in the context of a phonetically conditioned gmm speaker verification system. previous work has shown performance advantages through phonetic conditioning, but this has not been shown to date with the jfa framework. our focus is particularly on strategies for combining the phone-conditioned systems. we show that the classic fusion of the scores is suboptimal when using multiple gmm systems. we investigate several combination strategies in the model space, and demonstrate improvement over score-level combination as well as over a non-phonetic baseline system. this work was conducted during the 2008 clsp workshop at johns hopkins university.
optimal learning of p-layer additive f0 models with cross-validation. in this paper, we present the derivation of the backfitting training algorithms for generic p-layer additive f0 models for arbitrary positive integer p. we have presented the special cases of the algorithms with p = 2 and p = 3 that have been successfully applied to the modelings of japanese and english f0 contours, whereas the derivation of the algorithm was presented only for the two-layer case. the additive f0 model have smoothing parameters that establish a trade-off between the fit to the training data and the smoothness of the fitted curves, which have been all set to unity in the previous works. in this paper, we also present an optimal approach to set the values of these parameters using cross validation. we performed the training using the boston university radio news corpus and confirmed the effectiveness of the proposed method.
providing invariance to nonlinear valumetric scaling for quantization basedwatermarking. a novel framework providing invariance to a class of nonlinear valumetric distortions, such as gamma correction, for qim-based watermarking techniques [1] is presented. valumetric distortions are quite common in image and video processings and the sensitivity to valumetric scalings represents the main weakness of the watermarking techniques belonging to the quantization based class. the proposed method amounts to perform a mapping of the host samples in a proper transformed domain where the watermark is subsequently embedded using a gain invariant qim-based technique. the effectiveness of this approach has been verified by applying the watermarking system using rdm [2], aqim [3] and dm in the logarithmic domain [4] as embedding algorithms. simulation results provide a useful comparison of the performance of these different techniques within the proposed scheme as well as they confirm the invariance to nonlinear distortions.
emotion-based music retrieval on a well-reduced audio feature space. music expresses emotion. a number of audio extracted features have influence on the perceived emotional expression of music. these audio features generate a high-dimensional space, on which music similarity retrieval can be performed effectively, with respect to human perception of the music-emotion. however, the real-time systems that retrieve music over large music databases, can achieve order of magnitude performance increase, if applying multidimensional indexing over a dimensionally reduced audio feature space. to meet this performance achievement, in this paper, extensive studies are conducted on a number of dimensionality reduction algorithms, including both classic and novel approaches. the paper clearly envisages which dimensionality reduction techniques on the considered audio feature space, can preserve in average the accuracy of the emotion-based music retrieval.
ensembles of landmark multidimensional scalings. landmark multidimensional scaling (lmds) uses a subset of data (landmark points) to solve classical mds, where the scalability is increased but the approximation is noise-sensitive. in this paper we present an ensemble of lmdss, referred to as landmark mds ensemble (lmdse), where we use a portion of the input in a piecewise manner to solve classical mds, combining individual lmds solutions which operate on different partitions of the input. ground control points (gcps) that are shared by partitions considered in the ensemble, allow us to align individual lmds solutions in a common coordinate system through affine transformations. lmdse solution is determined by averaging aligned lmds solutions. we show that lmdse is less noise-sensitive while maintaining the scalability as well as the speed of lmds. experiments on synthetic data (noisy grid) and real-world data (similar image retrieval) confirm the high performance of the proposed lmdse.
a systolic array for linear mimo detection based on an all-swap lattice reduction algorithm. a systolic array to implement lattice-reduction-aided linear detection is proposed for a mimo receiver. the lattice reduction algorithm and the ensuing linear detections are operated in the same array, which can be hardware-efficient. all-swap lattice reduction algorithm (aslr) is considered for the systolic design. aslr is a variant of the lll algorithm, which processes all lattice basis vectors within one iteration. lattice-reduction-aided linear detection based on aslr and lll algorithms have very similar bit-error-rate performance, while aslr is more time efficient in the systolic array, especially for systems with a large number of antennas.
estimation of mb steganography based on least square method. in the available jpeg steganography methods, model-based (mb) steganography technique is more secure than jsteg, f5 and outguess. in this paper, we consider the problem of estimating the embedded length of secret messages of mb steganography. we provide an expected distribution of the non-zero ac coefficients' high precision histogram to fit the stegotexts', and adopt the cropped and recompressed calibration to obtain the carrier image approximation. then, we present a new algorithm to estimate the embedding rates based on least square method. the attacks towards mb are successful with experimental evidence on 2700 carrier images and the stego images of different quality factor and different embedding rates.
microarray classification using block diagonal linear discriminant analysis with embedded feature selection. in this paper, block diagonal linear discriminant analysis (bdlda) is improved and applied to gene expression data. bdlda is a classification tool with embedded feature selection, that has demonstrated good performance on simulated data. however, by using cross validation in training, bdlda is time consuming, thus not an appropriate algorithm for gene expression data, which has a large number of features and relatively small number of samples. in our algorithm, estimated error rate is used as a measure to choose the best model. the algorithm is optimized by repeating the model construction procedure with previously selected features removed, which leads to increased classification robustness. our algorithm is tested using 10 fold cross validation. in most simulated and real data, our method outperforms the state-of-the-art techniques, showing promise for its use in microarray classification problems. the resulting block structure allows to identify discriminating correlated genes, which is potentially useful in cancer research.
panorama recovery from noisy uav surveillance video. this paper proposes an efficient and robust algorithm to recover a panorama from poorly-obtained uav video frames contaminated with significant noise. in this algorithm, the eigen-space based neighborhood region will be introduced with our novel feature-based random m least-sqaures (rmls) registration technique. meanwhile, the corresponding similarity regions will be assigned weights according to the relativity between these neighboring regions. next, bayesian multi-frame sampling will be implemented utilizing the homography estimated by the frame registration. finally, the sub-region in each frame which is applicable to the multi-frame sampling will be stitched utilizing multi-resolution blending.
musical noise generation analysis for noise reduction methods based on spectral subtraction and mmse stsa estimation. in this paper, we reveal new findings about the generated musical noise in minimum mean-square error short-time spectral amplitude (mmse stsa) processing. recently we have proposed a objective metric of musical noise based on kurtosis change ratio on spectral subtraction (ss). also we found an interesting relationship among the degree of generated musical noise, the shapes of signal-s probability density function, the strength parameter of ss processing. this paper is aimed to automatically evaluate the sound quality of various types of noise reduction methods using kurtosis change ratio. we give a mathematical analysis based on higher-order statistics viewpoint, and lead to a valuable relation in that mmse stsa has a weakness in speech period distortion rather than noise period, and vice versa in ss.
bayesian discriminative adaptation for speech recognition. linear transform-based speaker adaptation is a standard part of many speech recognition systems. for unsupervised adaptation maximum likelihood estimation is typically used, as discriminative transforms are more heavily biased towards the supervision hypothesis which may contain errors. in this work a bayesian framework for discriminative adaptation is investigated. this reduces the hypothesis bias and allows robust estimates even with a limited amount of data. various forms of discriminative maximum-a-posteriori estimation, and associated issues, are detailed. to address these problems, the use of discriminative mapping transforms is also described. the proposed framework is evaluated on an english conversational speech task.
online estimation of the optimum quadratic kernel size of second-order volterra filters using a convex combination scheme. this paper presents a method for estimating the optimum memory size for identification of an unknown second-order volterra kernel. as these structures may imply considerable computational demands, it is highly desirable to design adaptive realizations with a minimum number of coefficients. therefore, we propose a combination scheme comprising two volterra filters with time-variant sizes of the actually used quadratic kernels. by following some simple rules, the number of diagonals in the quadratic kernels is increased or decreased in order to find the optimum memory configuration in parallel to the coefficient adaptation. thus, the arbitrary choice of the nonlinear system size is overcome by a dynamically growing/shrinking system. experimental results for various signals and nonlinear scenarios demonstrate the effectiveness of the proposed method.
functional estimation in hilbert space for distributed learning in wireless sensor networks. in this paper, we propose a distributed learning strategy in wireless sensor networks. taking advantage of recent developments on kernel-based machine learning, we consider a new sparsification criterion for online learning. as opposed to previously derived criteria, it is based on the estimated error and is therefore is well suited for tracking the evolution of systems over time. we also derive a gradient descent algorithm, and we demonstrate its relevance to estimate the dynamic evolution of temperature in a given region.
an analysis of articulatory-acoustic data based on articulatory strokes. an articulatory gestural unit representation called “articulatory stroke” is introduced. it aims to capture the constriction formation and release of a speech articulator such as the tongue tip. in that regard, the articulatory stroke is an attempt at a practical realization of the abstract articulatory gestures central to articulatory phonology. in this study we focus on the articulatory strokes associated with the critical articulator that is essential to realize a target phone. the critical articulatory stroke is parameterized in order to investigate the predictability of the parameters from phonetic contexts and to check the statistical dependency of acoustic changes associated with the critical articulatory strokes. canonical correlation analysis between the articulatory strokes and δmfccs showed that the critical articulatory strokes are more responsible to the acoustic changes inside target phonemes than non-critical articulators. this implies that modeling acoustic changes due to critical articulations could provide an edge in improving asr performance.
exploiting statically schedulable regions in dataflow programs. dataflow descriptions have been used in a wide range of digital signal processing (dsp) applications, such as multi-media processing, and wireless communications. among various forms of dataflow modeling, synchronous dataflow (sdf) is geared towards static scheduling of computational modules, which improves system performance and predictability. however, many dsp applications do not fully conform to the restrictions of sdf modeling. more general dataflow models, such as cal [1], have been developed to describe dynamically-structured dsp applications. such generalized models can express dynamically changing functionality, but lose the powerful static scheduling capabilities provided by sdf. this paper focuses on detection of sdf-like regions in dynamic dataflow descriptions — in particular, in the generalized specification framework of cal. this is an important step for applying static scheduling techniques within a dynamic dataflow framework. our techniques combine the advantages of different dataflow languages and tools, including cal [1], dif [2] and cal2c [3]. the techniques are demonstrated on the idct module of mpeg reconfigurable video coding (rvc).
law recognition via histogram-based estimation. in this paper, we study the problem of recognizing an unknown probability density function from one of its sample which is of interest in signal and image processing or telecommunication applications. by opposition with the classical kolmogorov-smirnov method based on empirical cumulative functions, we consider histogram estimators of the density itself built from our data. those histograms are generated via model selection, more specifically via a codelength-based information criterion. from the histograms, we may compute a kullback-leibler distance to any theoretical law which is used to complete the recognition. we apply this histogram-based method for law recognition in a theoretical setup where the true density is known as well as in a real setup where data come from radio channel propagation experimentation.
gray-scale erosion algorithm based on image bitwise decomposition: application to focal plane processors. a novel approach to implement gray-scale morphological operations is presented in this work. this new technique is based on the bitwise decomposition of the gray-scale image, yielding bitplanes disposed according to their bit of significance. it is of particular interest for implementations on focal plane processors. our approach relies on the binary search method to obtain either the maximum or minimum on a local neighborhood by manipulating the binary levels resulting from the bitwise decomposition with simple logic functions. this contrasts significantly with the classical threshold decomposition (td) approach, on which most of the current techniques are based on. our method shows better efficiency than td implementations. further gains can be obtained because our method shows a strong dependency on the image dynamic range.