waashk/dblp huggingface dataset:
independent vector analysis incorporating active and inactive states. independent vector analysis (iva) is a method for separating convolutedly mixed signals that avoids the well-known permutation problem in frequency domain blind source separation (bss). in this paper, we exploit the nonstationarity of signals, a common feature, for bss. one common type of nonstationarity, especially in speech, is that the signal can have silence periods intermittently, hence varying the set of active sources with time. to deal with such situations, we propose a novel state-based iva algorithm. moreover, we consider additive noise in our model. computer simulations are conducted to compare the proposed method with the standard iva and the results compare favorably.
automatic pronunciation verification of english letter-names for early literacy assessment of preliterate children. children need to master reading letter-names and letter-sounds before reading phrases and sentences. pronunciation assessment of letter-names and letter-sounds read aloud is an important component of preliterate children's education, and automating this process can have several advantages. the goal of this work was to automatically verify letter-names spoken by kindergarteners and first graders in realistic classroom noise conditions. we applied the same techniques developed in our previous work on automatic letter-sound verification by comparing and optimizing different acoustic models, dictionaries, and decoding grammars. our final system was unbiased with respect to the child's grade, age, and native language and achieved 93.1% agreement (0.813 kappa agreement) with human evaluators, who agreed among themselves 95.4% of the time (0.891 kappa).
statistics for complex random variables revisited. complex random signals play an increasingly important role in array, communications, and biomedical signal processing and related fields. however, the mathematical foundations of complex-valued signals and tools developed for handling them are scattered in literature. there appears to be a need for a concise, unified, and rigorous treatment of such topics. in this paper such a treatment is provided. moreover, we establish connections between seemingly unrelated objects such as real differentiability and circularity. in addition, a novel complex-valued extension of taylor series is presented and a measure for circularity is proposed.
efficient musical noise suppression for speech enhancement system. noise reduction techniques that are relying on spectral weighting rules often generate annoying musical noise artifacts in the processed signal. in this paper, we present a postfilter (pf) for the spectral weighting gains that is capable of reducing musical noise in a simple but efficient way. it includes a robust detector for speech pauses and low snr conditions and adaptively smoothes the weighting gains over frequency based on soft-decisions. objective and subjective measurements show consistent improvements if the postfilter is applied to conventional noise reduction techniques.
noise adaptive training using a vector taylor series approach for noise robust automatic speech recognition. in traditional methods for noise robust automatic speech recognition, the acoustic models are typically trained using clean speech or using multi-condition data that is processed by the same feature enhancement algorithm expected to be used in decoding. in this paper, we propose a noise adaptive training (nat) algorithm that can be applied to all training data that normalizes the environmental distortion as part of the model training. in contrast to the feature enhancement methods, nat estimates the underlying “pseudo-clean” model parameters directly without relying on point estimates of the clean speech features as an intermediate step. the pseudo-clean model parameters learned with nat are later used with vector taylor series (vts) model adaptation for decoding noisy utterances at test time. experiments performed on the aurora 2 and aurora 3 tasks, demonstrate that the proposed nat method obtain relative improvements of 18.83% and 32.02%, respectively, over vts model adaptation.
an objective measure for the musical noise assessment in noise reduction systems. in this paper, a new objective measure is proposed to assess the amount of the wide-band musical noise in an acoustic signal. the measure uses time and frequency characteristics of the musical noise to determine the musicalness of the residual noise. the proposed measure uses noise masking thresholds to measure how much audible the musical noise components are. furthermore, a harmonicity measure is used to discriminate between the musical noise components and the speech components. at the end, the correlation of the proposed measure with the subjective listening test results is calculated and its performance is verified in experiments.
speaker diarization using unsupervised discriminant analysis of inter-channel delay features. when multiple microphones are available estimates of inter-channel delay, which characterise a speaker-s location, can be used as features for speaker diarization. background noise and reverberation can, however, lead to noisy features and poor performance. to ameliorate these problems, this paper presents a new approach to the discriminant analysis of delay features for speaker diarization. this novel and nonetheless unsupervised approach aims to increase speaker separability in delay-space. we assess the approach on subsets of four standard nist rt datasets and demonstrate a relative improvement in diarization error rate of 25% on a separate evaluation set using delay features alone.
a spatial adaptive filter for smoothing of non-gaussian texture noise. this paper contributes a novel technique for reducing the interference of non-gaussian texture noise from images. since the inherent properties of texture noise are very different from those of gaussian white noise, the basic assumption of conventional image denoising techniques is invalid. here we present a spatial adaptive filtering scheme to remove non-gaussian texture noise from textile images based on local and non-local similarities. in order to exploit the high correlations among pixels, pixels with uniform texture local regions are estimated differently from those pixels located near edges, that is, for points located in local uniform texture regions, gaussian weighted averaging of their neighbors can achieve the adaptive effect of the human visual system, whereas for edge points, to find pixels with similar local statistics both in the vicinity and far away can produce a sufficient set of pixels for reasonable averaging. this filtering strategy is applied to textile images corrupted by texture noise and the performance is demonstrated to outperform current state-of-art image denoising techniques. non-gaussian
predicting the progress and the peak of an epidemic. the problem is statistical prediction of the number of people that will be infected with a contagious illness in a closed population over time. the prediction is based on the susceptible-infectious-recovered (sir) model of epidemic dynamics with inhomogeneous population mixing. the paper presents a theoretical analysis of the predictive accuracy based on the cramér-rao lower bound (crlb). the crlb provides a tool that enables us to quantify the prediction accuracy of a scale of an epidemic as a function of the prior uncertainty of sir model parameters, measurement accuracy of the number of infected people and the amount of data available for processing. a verification of the theoretical analysis is carried out by monte carlo simulations.
alternatives to spherical microphone arrays: hybrid geometries. we present a novel theory and design for constructing microphone arrays to extract spherical harmonic components from soundfields. the proposed non-spherical array structure provides a flexible and alternative design to the traditional spherical microphone arrays with lesser restriction on sensor locations. we use the properties of the associated legendre functions and the spherical bessel functions to develop a systematic approach to place circular microphone arrays in three dimensions for hybrid array geometries. as an illustration, we design and simulate a fifth order spherical harmonic decomposition array using 70 microphones to operate over a frequency band of an octave.
a binary wavelet-based scheme for grayscale image compression. this paper proposes a novel grayscale image compression approach using the binary wavelet transform (bwt) and context-based arithmetic coding, namely the context-based binary wavelet transform coding algorithm (cbwtc). in our cbwtc, in order to alleviate the degradation of predictability caused by the bwt and eliminate the correlation within the same level subbands, three highpass wavelet coefficients at the same location are combined to form an octave symbol and then encoded with a ternary arithmetic coder. the conditional context of the cbwtc is properly modeled by exploiting the properties of the bwt as well as taking the advantages of non-causal adaptive context modeling. experimental results show that the coding performance of the cbwtc is better than that of the state-of-the-art grayscale image coders except for images containing rich texture, and always outperforms the jbig2 algorithm and other bwt-based binary coding technique.
normalized minimum-redundancy and maximum-relevancy based feature selection for speaker verification systems. in this paper, an information theoretical approach to select features for speaker recognition systems is proposed. conventional approaches having a fixed interval of analysis frames are not appropriate to represent dynamically varying characteristics of speech signals. to maximize the speaker-related information varied by the characteristics of speech signals, we propose an information theory based feature selection method where features are selected to have minimum-redundancy with in selected features but maximumrelevancy to training speaker models. experimental results verify that the proposed method reduces the error rates of speaker verification systems by 27.37 % in nist 2002 database.
fast and efficient dimensionality reduction using structurally random matrices. structurally random matrices (srm) are first proposed in [1] as fast and highly efficient measurement operators for large scale compressed sensing applications. motivated by the bridge between compressed sensing and the johnson-lindenstrauss lemma [2] , this paper introduces a related application of srms regarding to realizing a fast and highly efficient embedding. in particular, it shows that a srm is also a promising dimensionality reduction transform that preserves all pairwise distances of high dimensional vectors within an arbitrarily small factor ∈, provided that the projection dimension is on the order of o(∈−2 log3 n), where n denotes the number of d-dimensional vectors. in other words, srm can be viewed as the sub-optimal johnson-lindenstrauss embedding that, however, owns very low computational complexity o(d log d) and highly efficient implementation that uses only o(d) random bits, making it a promising candidate for practical, large scale applications where efficiency and speed of computation are highly critical.
objective evaluation of english learners' timing control based on a measure reflecting perceptual characteristics. automatic evaluation of english timing control proficiency is carried out by comparing segmental duration differences between learners and reference native speakers. to obtain an objective measure matched to human subjective evaluation, we introduced a measure reflecting perceptual characteristics. the proposed measure evaluates duration differences weighted by the loudness of the corresponding speech segment and the differences or jumps in loudness from the two adjacent speech segments. experiments showed that estimated scores using the new perception-based measure provided a correlation coefficient of 0.72 with subjective evaluation scores given by native english speakers on the basis of naturalness in timing control. this correlation turned out to be significantly higher than that of 0.54 obtained when using a simple duration difference measure.
an em algorithm for scfg in formal syntax-based translation. in this paper, we investigate the use of bilingual parsing on parallel corpora to better estimate the rule parameters in a formal syntax-based machine translation system, which are normally estimated from the inaccurate heuristics. we use an expectation-maximization (em) algorithm to re-estimate the parameters of synchronous context-free grammar (scfg) rules according to the derivation knowledge from parallel corpora based on maximum likelihood principle, rather than using only the heuristic information. the proposed algorithm produces significantly better bleu scores than a state-of-the-art formal syntax-based machine translation system on the iwslt 2006 chinese to english task.
improved clustered hierarchical tandem system with bottom-up processing. the outputs of multi-layer perceptron (mlp) classifiers have been successfully used in tandem systems as features for hmm-based automatic speech recognition. in a previous paper, we proposed data-driven clustered hierarchical mlp (chmlp) tandem system yielding improved performance by dividing the complicated global phone classification problem into simpler hierarchical tasks, in which specialized mlps are trained to classify small clusters of confusing phones in a hierarchical structure. in this paper a bottom-up processing is further proposed to enhance the classification in the above chmlp and offer even better performance. mlp rescoring for the tandem system is also investigated. the best result achieved 19.1% relative error reduction over the mfcc baseline.
flicker noise mitigation in direct-conversion receivers for ofdm systems. direct-conversion receiver (dcr) architecture has received considerable attention recently owing to its portable architecture and superior performance in terms of power and cost over its super-heterodyne counterpart. flicker noise is one of the major impairments which severely affects the performance of the system. in this work, we investigate the use of signal processing techniques in the mitigation of flicker noise in ofdm-based systems which employ dcr architecture. the statistical properties of flicker noise are exploited to develop adaptive signal processing algorithms that reduce the effect of flicker noise in dcrs. results indicate that signal processing algorithms can provide significant performance gain under low snr conditions.
on the pts method and ber-minimizing power allocation of the multi-channel ofdm system. in multiple-access multicarrier systems, it is possible to serve multiple users at high data rates from a single base station. in this paper, we propose the multi-channel partial transmit sequences (mcpts) method for multiple-access ofdm peak-to-average power ratio (par) reduction. by applying the pts phase rotations to each access channel separately and assuming that the rotations can be detected by each user as a channel fading, mcpts avoids two major disadvantages of standard pts: i) the phase rotations can be detected without side information, and ii) the set of possible phase rotation values does not need to be finite. in addition, an iterative method of joint mcpts and power allocation is proposed to minimize the average ber. the results show a significant ber improvement.
visual saliency with side information. we propose novel algorithms for organizing large image and video datasets using both the visual content and the associated side-information, such as time, location, authorship, and so on. earlier research have used side-information as pre-filter before visual analysis is performed, and we design a machine learning algorithm to model the join statistics of the content and the side information. our algorithm, diverse-density contextual clustering (d2c2), starts by finding unique patterns for each sub-collection sharing the same side-info, e.g., scenes from winter. it then finds the common patterns that are shared among all subsets, e.g., persistent scenes across all seasons. these unique and common prototypes are found with multiple instance learning and subsequent clustering steps. we evaluate d2c2 on two web photo collections from flickr and one news video collection from trecvid. results show that not only the visual patterns found by d2c2 are intuitively salient across different seasons, locations and events, classifiers constructed from the unique and common patterns also outperform state-of-the-art bag-of-features classifiers.
a highly robust audio hashing system using auditory-based front-end processing. in this paper, a robust perceptual audio hashing system is presented. a model of the human auditory system is used to extract robust features from the outputs of a non-linear filter bank that mimics the human basilar membrane. experiments on various audio excerpts show that this new ear-based front-end processing provides very effective hash values. the proposed audio hashing system performs very satisfactorily in identification and it turned out very resilient to a large variety of severe audio attacks.
improved svm speaker verification through data-driven background dataset collection. the problem of background dataset selection in svm-based speaker verification is addressed through the proposal of a new data-driven selection technique. based on support vector selection, the proposed approach introduces a method to individually assess the suitability of each candidate impostor example for use in the background dataset. the technique can then produce a refined background dataset by selecting only the most informative impostor examples. improvements of 13% in min. dcf and 10% in eer were found on the sre 2006 development corpus when using the proposed method over the best heuristically chosen set. the technique was also shown to generalise to the unseen nist 2008 sre corpus.
generating matrix of discrete fourier transform eigenvectors. this paper provides a novel method to obtain the eigenvectors of discrete fourier transform (dft), which are accurate approximations to the continuous hermite-gaussian functions (hgfs). the proposed method uses a generating matrix and an initial eigenvector. by multiplying the initial eigenvector with the generating matrix, we can derive a new eigenvector. repeating this procedure we can acquire all the eigenvectors. compare with the conventional o(n3) commutative matrix method, this new method can generate all the dft eigenvectors with complexity reduced to o(n2logn). the generating matrix can be further used to intensify the conventional commuting matrix. the simulation result shows that the hermite-gaussian like (hgl) eigenvectors of the strengthened commuting matrix outperform those of santhanam's.
face recognition and gender classification in personal memories. face annotation is an important concept for personal memories retrieval. using automatic face recognition to annotate and find people in those memories provides an improvement of a personal memories management system. however, its results are limited by the uncontrolled conditions inherent to personal memories. in this paper, we propose a face recognition method to address these limitations which includes techniques of skin detection and pose estimation. it is also proposed a gender classification method to provide more information about the detected faces. experimental results are presented that show that these methods improve the overall performance of face recognition and gender classification in personal memories.
a comparison between sequence kernels for svm speaker verification. we present a comparative study of several svm speaker verification (sv) systems based on sequence kernels: the gmm-supervectors kernel, the fisher kernel, the generalized linear discriminant sequence (glds) kernel, our feature space normalized sequence (fsns) kernel and a “novel” sequence kernel in sv, the correlation kernel. we also compare these svm systems to the conventional generative ubm-gmm. we carry out experiments on the nist'2005 sre evaluation set. the results show that the fsns system yields comparable performances to ubm-gmm and significantly outperforms glds. they also show that the gmm-supervectors system outperforms all the others. finally, they show that the best performances are achieved by fusing the fsns and the gmm-supervectors systems.
discriminating subsequent lane-crossing and driver-correction events using trajectory models of lateral slopes. in this paper, we propose a new framework to discriminate the initial maneuver of lane-crossing event from driver-correction event, which is the primary reason for false warnings of the lane departure prediction systems. the proposed algorithm validates the beginning episode of the trajectory of driving signals whether it will cause a lane crossing event or not, by employing driver behavior models of directional sequence of piecewise lateral slopes (dspls) representing lane-crossing and driver-correction events. the framework utilizes only common driving signals, and allows adaptation scheme of driver behavior models to better represent individual driving characteristics. the experimental evaluation shows that the proposed dspls has detection error as low as 17% equal error rate. furthermore, the proposed algorithm reduces the false alarm rate of the original lane departure prediction system from 38.8% to 6.1% with less trade-off for the prediction accuracy.
a fast feature extraction method. a fast subspace analysis and feature extraction algorithm is proposed which is based on fast haar transform and integral vector. in rapid object detection and conventional binary subspace learning, haar-like functions have been frequently used but true haar functions are seldom employed. in this paper we have shown that true haar functions can be successfully used to accelerate subspace analysis and feature extraction. both the training and testing speed of the proposed method is higher than conventional algorithms. experimental results on face database demonstrated its effectiveness.
asymmetric gradient-based image alignment. a new method for template-based image alignment is presented in this paper. a gradient-based optimization of motion compensated error difference is addressed to solve the image alignment problem. its novelty lies in the minimized error, which considers the bi-directional compensation of the reference template and the current image onto each other in a constrained asymmetrical fashion. the proposed approach is shown to be a generalization of previous state-of-the art gradient-based methods. an experimental evaluation is provided to show how the new method outperforms the former in presence of noisy images, and to give some insights into its properties.
stereo-based stochastic noise compensation based on trajectory gmms. this paper proposes a novel stereo-based stochastic noise compensation technique based on trajectory gmms. although the gmm-based noise compensation techniques such as splice work effective, their performance sometimes degrades due to the inappropriate dynamic characteristics caused by the frame-by-frame mapping. while the use of dynamic feature constraints on the mapping stage can alleviate this problem, it also introduces an inconsistency between training and mapping. the recently proposed trajectory gmm-based feature mapping technique can solve this inconsistency while keeping the benefits of the use of dynamic features, and offers an entire sequence-level transformation rather than the frame-by-frame mapping. results from a noise compensation experiment on the aurora-2 task show that the proposed trajectory gmm-based noise compensation technique outperforms the conventional ones.
asymptotic performance analysis of pca algorithms based on the weighted subspace criterion. this paper studies the asymptotic distribution of the eigenvectors estimated by some pca algorithms based on the weighted subspace criterion. this enables us to analyse how the choice of the weighting matrix affects the algorithm's performance, an issue previously overlooked.