waashk/dblp huggingface dataset:
combining vts model compensation and support vector machines. it is difficult to adapt discriminative classifiers, particularly kernel based ones such as support vector machines (svms), to handle mismatches between the training and test data. in previous work adaptation was performed by modifying the kernel used with the svm, rather changing the svm parameters themselves. however an idealised form of compensation, single pass retraining, was used to alter the generative models associated with the generative kernel. in this paper vector taylor series model compensation is used. this scheme is more efficient and allows a noise model to be estimated. the performance of the new scheme is evaluated on two continuous digit tasks. on both tasks svm-rescoring outperformed the baseline vts compensated models.
registration of multimodal data for estimating the parameters of an articulatory model. being able to animate a speech production model with articulatory data would open applications in many domains. in this paper, we first consider the problem of acquiring articulatory data from non invasive image and sensor modalities: dynamic ultrasound (us) images, stereovision 3d data, electromagnetic sensors and mri. we here especially focus on automatic registration methods which enable the fusion of the articulatory features in a common frame. we then derive articulatory parameters by fitting these features with maeda's model. to our knowledge, it is the first attempt to derive articulatory parameters from features automatically extracted and registered between the modalities. results prove the soundness of the approach and the reliability of the fused articulatory data.
on gmm kalman predictive coding of lsfs for packet loss. gaussian mixture model (gmm)-based kalman predictive coders have been shown to perform better than baselinegmm recursive coders in predictive coding of line spectral frequencies (lsfs) for both clean and packet loss conditions however, these stationary gmm kalman predictive coders were not specifically designed for operation in packet loss conditions. in this paper, we demonstrate an approach to the the design of gmm-based predictive coding for packet loss channels. in particular, we show how a stationary gmm kalman predictive coder can be modified to obtain a set of encoding and decoding modes, each with different kalman gains. this approach leads to more robust performance of predictive coding of lsfs in packet loss conditions, as the coder mismatch between the encoder and decoder are minimized. simulation results show that this robust gmm kalman predictive coder performs better than other baseline gmm predictive coders with no increase in complexity. to the best of our knowledge, no previous work has specifically examined the design of gmm predictive coders for packet loss conditions.
convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing. hyperspectral unmixing aims at identifying the hidden spectral signatures (or endmembers) and their corresponding proportions (or abundances) from an observed hyperspectral scene. many existing approaches to hyperspectral unmixing rely on the pure-pixel assumption, which may be violated for highly mixed data. a heuristic unmixing criterion without requiring the pure-pixel assumption has been reported by craig: the endmember estimates are determined by the vertices of a minimum-volume simplex enclosing all the observed pixels. in this paper, using convex analysis, we show that the hyperspectral unmixing by craig's criterion can be formulated as an optimization problem of finding a minimum-volume enclosing simplex (mves). an algorithm that cyclically solves the mves problem via linear programs (lps) is also proposed. some monte carlo simulations are provided to demonstrate the efficacy of the proposed mves algorithm.
parallel multi-frequency narrowband active noise control systems. this paper analyzes and optimizes narrowband active noise control systems using multiple second-order adaptive filters. theoretical analysis of the overall parallel structure is based on autocorrelation and cross-correlation matrices. analytical result shows the use of single error signal to update all adaptive filters with different input signals will introduce extra tonal components in the residual noise. an optimized algorithm that uses bandpass filters to split the fullband error signal into multiple bandlimited subband error signals according to the frequencies of reference signals is developed to improve steady-state performance.
annotating images by harnessing worldwide user-tagged photos. automatic image tagging is important yet challenging due to the semantic gap and the lack of learning examples to model a tag's visual diversity. meanwhile, social user tagging is creating rich multimedia content on the web. in this paper, we propose to combine the two tagging approaches in a search-based framework. for an unlabeled image, we first retrieve its visual neighbors from a large user-tagged image database. we then select relevant tags from the result images to annotate the unlabeled image. to tackle the unreliability and sparsity of user tagging, we introduce a joint-modality tag relevance estimation method which efficiently addresses both textual and visual clues. experiments on 1.5 million flickr photos and 10 000 corel images verify the proposed method.
capacity scaling of wireless networks with complex field network coding. network coding in wired networks has been shown to achieve considerable throughput gains relative to traditional routing networks. while the ergodic capacity of wireless multihop networks is unknown, the scaling of capacity with the number of nodes (n) has recently received increasing attention. while existing works mainly focus on networks with n source-destination pairs, this paper deals with capacity scaling in any-to-any wireless links, where each node communicates with all other nodes. complex field network coding (cfnc) is adopted at the physical layer to allow n nodes exchanging information with simultaneous transmissions from multiple sources. a hierarchical cfnc-based scheme is developed and shown to achieve asymptotically (as n → ∞) optimal quadratic capacity scaling in a dense network, where the area is fixed and the density of nodes increases. this is possible by dividing the network into many clusters, with each cluster sub-divided into many sub-clusters, hierarchically.
automatic voice assignment tool for instant casting movie system. in instant casting movie system, a personal cg character is automatically generated. the character resembles a participant in a face geometry and texture. however, the voice of a character was an alternative voice determined by the gender of the participant. therefore sometimes it's not enough to identify the personality of a cg character. in this paper, an automatic pre-scored voice assignment tool for a personal cg character is presented. voice is essential to identify a personal character as well as a face feature. our proposed system selects the most similar voice to the participants from voice database, and assigns it as a voice of cg character. voice similarity criterion is presented by combination of eight acoustic features. after assigning voice data to a personal character, the voice track is played back in synchronization with the movement of the cg character. 60 voice variations have been prepared to our voice database. validity of the assigned voice has been evaluated by mos value. the proposed method has achieved 68% of the theoretical figure that is calculated by preliminary experiments.
enhancement of reverberant speech using the celp postfilter. in this paper we investigate the application of adaptive postfiltering for the enhancement of reverberant speech. the considered method is commonly used in code excited linear prediction (celp) speech coding to lower the impact of quantization noise in the excitation signal and the spectral envelope. we show that the underlying additive noise model is accurate enough to enhance speech which is recorded in an enclosed space where the resulting early reflections are usually modeled as a convolutive distortion. by means of adaptive filtering, the amplitudes of the unwanted peaks in the excitation signal are attenuated and the signal components at the harmonic peaks are emphasized. both, single- and multi-channel dereverberation algorithms are proposed having a moderate computational complexity. experiments have shown that this approach is capable of reducing early reverberation and attenuate the “distance-effect” arising from room reflections.
a flat direct model for speech recognition. we introduce a direct model for speech recognition that assumes an unstructured, i.e., flat text output. the flat model allows us to model arbitrary attributes and dependences of the output. this is different from the hmms typically used for speech recognition. this conventional modeling approach is based on sequential data and makes rigid assumptions on the dependences. hmms have proven to be convenient and appropriate for large vocabulary continuous speech recognition. our task under consideration, however, is the windows live search for mobile (wls4m) task [1]. this is a cellphone application that allows users to interact with web-based information portals. in particular, the set of valid outputs can be considered discrete and finite (although probably large, i.e., unseen events are an issue). hence, a flat direct model lends itself to this task, making the adding of different knowledge sources and dependences straightforward and cheap. using e.g. hmm posterior, m-gram, and spotter features, significant improvements over the conventional hmm system were observed.
model assessment with kolmogorov-smirnov statistics. one of the most basic problems in science and engineering is the assessment of a considered model. the model should describe a set of observed data and the objective is to find ways of deciding if the model should be rejected. it seems that this is an ill-conditioned problem because we have to test the model against all the possible alternative models. in this paper we use the kolmogorov-smirnov statistic to develop a test that shows if the model should be kept or it should be rejected. we explain how this testing can be implemented in the context of particle filtering. we demonstrate the performance of the proposed method by computer simulations.
factor analysis-based information integration for arabic dialect identification. in this study, we propose a new factor analysis-based modeling technique to more clearly describe the composition of the supervector defined by the gmm model for dialect identification. the method utilizes knowledge types of information contained in the transcript file of the data. we evaluate the effects of the proposed modeling algorithm on a gmm-based arabic dialect identification system. in particular, we compare eigenchannel modeling and our proposed information integration modeling. we show that the proposed modeling can obtain a 4.23% relative eer reduction with the same total number of factors, and a 9.37% relative eer reduction with the same number of channel/session factors versus eigenchannel modeling.
bayesian large margin hidden markov models for speech recognition. this paper presents a bayesian learning approach to large margin classifier for hidden markov model (hmm) based speech recognition. we build the bayesian large margin hmms (blm-hmms) and improve the model generalization for handling unknown test environments. using blm-hmms, the variational bayesian hmm parameters are estimated by maximizing lower bound of a marginal likelihood over the uncertainties of hmm parameters. the bayesian large margin estimation is performed with frame selection mechanism, and is illustrated to meet the objective of support vector machines, i.e. maximal class margin and minimal training errors. the new objective function is not only interpreted as a discriminative criterion, but also feasible to deal with model selection and adaptive training. experiments on phone recognition show that blm-hmms perform better than other generative and discriminative models.
one-handed gesture recognition using ultrasonic doppler sonar. this paper presents a new device based on ultrasonic sensors to recognize one-handed gestures. the device uses three ultrasonic receivers and a single transmitter. gestures are characterized through the doppler frequency shifts they generate in reflections of an ultrasonic tone emitted by the transmitter. we show that this setup can be used to classify simple one-handed gestures with high accuracy. the ultrasonic doppler based device is very inexpensive - $20 usd for the whole setup including the acquisition system, and computationally efficient as compared to most traditional devices (e.g. video). these gestures, could potentially be used to control and drive a device.
compressive imaging of color images. in this paper, we propose a novel compressive imaging framework for color images. we first introduce an imaging architecture based on combining the existing single-pixel compressive sensing (cs) camera with a bayer color filter, thereby enabling acquisition of compressive color measurements. then we propose a novel cs reconstruction algorithm that employs joint sparsity models in simultaneously recovering the r, g, b channels from the compressive measurements. experiments simulating the imaging and reconstruction procedures demonstrate the feasibility of the proposed idea and the superior quality in reconstruction.
instrumentation analysis and identification of polyphonic music using beat-synchronous feature integration and fuzzy clustering. in this paper, a music instrumentation analysis and identification method is proposed. in contrast to existing systems, it tries to identify the whole instrument set in polyphonic music and also decide whether each instrument actually dominates at a particular moment or not, but without calculating the exact pitch, onset timing, or the volume of each note. moreover, it does not require the music source separation in advance. we address this problem by incorporating the beatsynchronous scheme with fuzzy clustering to analyze the instrument components. experiments show that the instrument identification process results in an 85.19% averaging recognition rate, which is comparable with other existing systems. in addition, it generates the extra time-varying instrumentation information. this information can be considered as a new mid-level feature in music information retrieval systems.
a post-processing technique for regeneration of over-attenuated speech. despite the success of recent speech enhancement algorithms, the enhanced signals still suffer from undesirable speech distortion caused by over-attenuation of weak speech spectral components. in this paper, a post-processing technique based on the regeneration of both voiced and unvoiced speech is proposed to alleviate this problem. a non-linear transformation is first applied to awiener filtered speech and the transformed signal is multiplied by a pre-estimated spectral envelop to form the regenerated speech. the resulting speech is then obtained using a weighted combination of the regenerated speech components and the filtered speech. this process significantly improves the resulting speech quality as compared to the original filtered version. it results in speech that sounds less lowpassed. also, the residual musical noise is significantly masked by the regenerated speech components. objective measures show that the quality of the resulting speech is much closer to the clean speech as compared to the original wiener filtered speech.
a pde characterization of the intrinsic mode functions. for the first time, a proof of the sifting process (sp) and so the empirical mode decomposition (emd), is given. for doing this, lower and upper envelopes are modeled in a more convenient way that helps us prove the convergence of the sp towards a solution of a partial differential equation (pde). we also prove that such a pde has a unique solution, which ensures the uniqueness of the emd decomposition. the new formulation of envelopes has another benefit. in fact, it removes interpolation problems and related issues. not only helps the modelization of envelopes to give a mathematical framework on the emd, but also, as confirmed by the numerical simulations, the pde-based emd improves a lot the classical emd.
new hybrid adaptive blind equalization algorithms for qam signals. this paper introduces new hybrid blind equalization algorithms for qam signals, the first term of which is the constant modulus criterion (cma) or its extended version (ecma) and the second are a penalty term that vanishes at constellation points coordinates. several penalties, based on cosine, gaussian and polynomial ℓ1-norm fonctions respectively are investigated. simulations show the effectiveness of these algorithms.
progressive lossy-to-lossless coding of arbitrarily-sampled image data using the modified scattered data coding method. in earlier work, demaret and iske proposed the scattered data coding (sdc) method for (single-rate) coding of arbitrarily-sampled image data. in this paper, several modifications to the sdc method are proposed in order to remove some limitations of the original scheme, improve coding efficiency, and add a progressive lossy-to-lossless coding capability. through experimental results, the proposed method is shown to yield a significant improvement in coding efficiency (relative to the original sdc method) as well as provide an efficient progressive lossy-to-lossless coding capability.
spatial filtering using directional audio coding parameters. directional audio coding (dirac) is a recent method for spatial audio processing, based on a perceptually motivated representation of spatial sound. due to its efficiency, dirac has already been proposed for spatial audio teleconferencing scenarios. modern hands-free communication systems usually include beamforming techniques to improve speech intelligibility by suppressing diffuse background noise and interfering sources. in this paper, we propose a novel spatial filtering method which can be integrated into the dirac spatial codec. it uses a spectral weighting of the recorded audio signal, where the design of the corresponding spatial filter transfer function is based on the dirac parameters, i. e., direction-of-arrival and diffuseness of the sound field. simulation results show that compared to a standard beamformer the novel technique offers significantly higher interference attenuation, while introducing similar distortion of the desired signal.
modified mpe/mmi in a transducer-based framework. in this paper we show how common training criteria like for example mpe or mmi can be extended to incorporate a margin term. in addition, a transducer-based training implementation is presented, which covers a large variety of discriminative training criteria for asr, including the standard mmi, mpe, and mce criteria, as well as the modifications to these criteria presented here. the modified criteria are directly related with the conventional large margin formulation of svms. in the proposed approach, we can take advantage of the generalization guarantees of large margin classifiers while keeping the existing framework for the discriminative training, including the efficient algorithms for conventional mpe or mmi. on the conceptual side, this allows for a direct evaluation of the margin term. finally, experimental results are presented for different large vocabulary continuous speech recognition tasks (one of which is trained on a very large amount of training data) using these modified criteria.
separation of layers from images containing multiple reflections and transparency using cyclic permutation. in the paper, we propose a new method for blind separation of an arbitrary number of images from a set of their linear mixtures with unknown coefficients. this approach is as follows. we first introduce a novel multiple correlation between one image and a set of multiple images. then this multiple correlation leads us to provide a set of simultaneous linear equations for updating each mixture of images. finally, source images are recovered by iterating between solving the sets of equations and cyclically permuting the mixtures of images. the technique can be applied for extracting multiple layers from images containing multiple reflections and transparency.
minimum-disturbance description for the development of adaptation algorithms and a new leakage least squares algorithm. usual methods for the development of adaptive filters are based on a stochastic approximation of the gradient vector and hessian matrix, or on a deterministic minimization of quadratic a posteriori output errors. gradient-based algorithms are usually placed in the first group, whereas least squares (ls) based algorithms are placed in the second group. these are just how algorithms are usually presented and analyzed and alternative descriptions exit. this paper proposes to shed new light onto known adaptation algorithms by means of a minimum-disturbance approach to the cost function together with constraints added to improve their robustness. the resulting algorithms are able to perform extremely well in many demanding applications.
a fast, accurate approximation to log likelihood of gaussian mixture models. it has been a common practice in speech recognition and elsewhere to approximate the log likelihood of a gaussian mixture model (gmm) with the maximum component log likelihood. while often a computational necessity, the max approximation comes at a price of inferior modeling when the gaussian components significantly overlap. this paper shows how the approximation error can be reduced by changing component priors. in our experiments the loss in word error rate due to max approximation, albeit small, is reduced by 50–100% at no cost in computational efficiency. furthermore, we expect acoustic models will become larger with time and increase component overlap and word error rate loss. this makes reducing the approximation error more relevant. the techniques considered do not use the original data and can easily be applied as a post-processing step to any gmm.
svm-based state transition framework for dynamical human behavior identification. this investigation proposes an svm-based state transition framework (named as stsvm) to provide better performance of discriminability for human behavior identification. the stsvm consists of several state support vector machines (ssvm) and a state transition probability (stpm). the intra-structure information and inter-structure information of a human activity are analyzed and correlated by the ssvm and stpm, respectively. the integration of the ssvm and the stpm effectively provides human behavior understanding. with a database consisting of five kinds of human behaviors: raising hand, standing up, squatting down, falling down, and sitting, the proposed algorithm has been demonstrated with a significant recognition rate of 88.6%.
regression-based clustering for hierarchical pitch conversion. this study presents a hierarchical pitch conversion method using regression-based clustering for conversion function modeling. the pitch contour of a speech utterance is first extracted and decomposed into sentence-, word and sub-syllable-level features in a top-down mechanism. the pair-wise source and target pitch feature vectors at each level are then clustered to generate the pitch conversion function. regression-based clustering, which clusters the feature vectors to achieve a minimum conversion error between the predicted and the real feature vectors is proposed for conversion function generation. a classification and regression tree (cart), incorporating linguistic, phonetic and source prosodic features, is adopted to select the most suitable function for pitch conversion. several objective and subjective evaluations were conducted and the comparison results to the gmm-based methods for pitch conversion confirm the performance of the proposed regression-based clustering approach.