waashk/dblp huggingface dataset:
active learning for semi-supervised multi-task learning. we present an algorithm for active learning (adaptive selection of training data) within the context of semi-supervised multi-task classifier design. the semi-supervised multi-task classifier exploits manifold information provided by the unlabeled data, while also leveraging relevant information across multiple data sets. the active-learning component defines which data would be most informative to classifier design if the associated labels are acquired. the framework is demonstrated through application to a real landmine detection problem.
a novel rejection sampling scheme for posterior probability distributions. rejection sampling (rs) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated monte carlo techniques. the main limitation to the use of rs is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. there are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. in this paper we adopt a bayesian view of the problem and propose a general rs scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. the method enables the analytical calculation of the bound and can be applied to a large class of target densities. we illustrate its use with a simple numerical example.
automatic named identification of speakers using diarization and asr systems. in this paper, we consider the extraction of speaker identity from audio records of broadcast news without a priori acoustic information about speakers. using an automatic speech recognition system and an automatic speaker diarization system, we present improvements for a method which allows to extract speaker identities from automatic transcripts and to assign them to speech segments. experiments are carried out on french broadcast news records from the ester 1 evaluation campaign. experimental results using outputs of automatic speech recognition and automatic diarization are presented.
on acoustic surveillance of hazardous situations. the present study presents a practical methodology for automatic space monitoring based solely on the perceived acoustic information. we consider the case where atypical situations such as screams, explosions and gunshots take place in a metro station environment. our approach is based on a two stage recognition schema, each one exploiting hmms for approximating the density function of the corresponding sound class. the main objective is to detect abnormal events that take place in a noisy environment. a thorough evaluation procedure is carried out under different snr conditions and we report high detection rates with respect to false alarm and miss probabilities rates.
cart-based modeling of chinese tonal patterns with a functional model tracing the fundamental frequency trajectories. we propose an approach to modeling chinese tonal patterns, focusing on the basic fundamental frequency (f0) patterns characterized by the contextual linguistic features that can be directly extracted from text. we analyze tonal patterns as sparse target points (tonal f0 peaks and valleys) and represent them in parametric form within the framework of a functional f0 model. the relationships between the target points and underlying linguistic features are trained using classification and regression tree analysis (carts), and this functional model is used to trace the f0 trajectories when training the carts and to synthesize a tonal pattern from the target points predicted by the carts. our experiments indicate that the proposed method has low f0 prediction errors. utilization of the f0 ranges measured from training samples could significantly reduce the influences of differences in voice ranges on training a speaker-independent model. furthermore, the most important roles in characterizing tonal patterns were played by a few linguistic features such as lexical tone context and the distinction between voiced from unvoiced initials.
joint reconstruction of compressed multi-view images. this paper proposes a distributed representation algorithm for multi-view images that are jointly reconstructed at the decoder. compressed versions of each image are first obtained independently with random projections. the multiple images are then jointly reconstructed by the decoder, under the assumption that the correlation between images can be represented by local geometric transformations. we build on the compressed sensing framework and formulate the joint reconstruction as a l2-l1 optimization problem. it tends to minimize the mse distortion of the decoded images, under the constraint that these images have sparse and correlated representations over a structured dictionary of atoms. simulation results with multi-view images demonstrate that our approach achieves better reconstruction results than independent decoding. moreover, we show the advantage of structured dictionaries for capturing the geometrical correlation between multi-view images.
design of large planar diaphragm incorporating multiple vibrators for sound directivity control via fem and bem. we have realized a sound directivity control of a large planar diaphragm by controlling the bending vibrations using multiple vibrators. this paper proposes a method to determine the parameters such as the shape and thickness of the diaphragm and the position of the vibrators. in this method, the finite element method (fem) is used to simulate the diaphragm vibrations and the boundary element method (bem) is used to simulate the radiated sound. we first validate the simulation results by measuring the actual bending vibrations and sound directivity and comparing them with the simulated results. we then show that metaheuristics are effective in finding appropriate parameters because the sound directivity largely varies in a continuous manner with variations in the parameters.
discriminative training of hierarchical acoustic models for large vocabulary continuous speech recognition. in this paper we propose discriminative training of hierarchical acoustic models for large vocabulary continuous speech recognition tasks. after presenting our hierarchical modeling framework, we describe how the models can be generated with either minimum classification error or large-margin training. experiments on a large vocabulary lecture transcription task show that the hierarchical model can yield more than 1.0% absolute word error rate reduction over non-hierarchical models for both kinds of discriminative training.
particle filtering for quantized innovations. in this paper, we re-examine the recently proposed distributed state estimators based on quantized innovations. it is widely believed that the error covariance of the quantized innovation kalman filter [1, 2] follows a modified riccati recursion. we present stable linear dynamical systems for which this is violated and the filter diverges. we propose a particle filter that approximates the optimal nonlinear filter and observe that the error covariance of the particle filter follows the modified riccati recursion of [1]. we also simulate a posterior cramer-rao bound (pcrb) for this filtering problem.
adaptive quickest change detection with unknown parameter. quickest detection of an abrupt distribution change with an unknown time varying parameter is considered. a novel adaptive approach is proposed to tackle this problem, which is shown to outperform the celebrated parallel cusum test. performance is evaluated through theoretical analysis and numerical simulations.
sequential best range matching: an error concealment technique with application in high packet loss image transmission. suffering from the inadequacy of reliable received data and their lack of sufficient a priori knowledge about the lost regions, the existing error concealment (ec) techniques fail to perform well in high packet loss transmission of compressed images. in order to obviate such a deficiency, we propose the sequential best range matching (sbrm) algorithm: the correctly received regions of image are employed to fabricate the lost regions according to the spatial similarities within the image. our simulation results show that sbrm not only outperforms the existing ec methods especially in high packet loss conditions, but also provides suitable prior knowledge to initialize further estimation and denoising processes.
image spam filtering using fourier-mellin invariant features. image spam is a new obfuscating method which spammers invented to more effectively bypass conventional text based spam filters. in this paper, a framework for filtering image spams by using the fourier-mellin invariant features is described. fourier-mellin features are robust for most kinds of image spam variations. a one-class classifier, the support vector data description (svdd), is exploited to model the boundary of image spam class in the feature space without using information of legitimate emails. experimental results demonstrate that our framework is effective for fighting image spam.
genre effects on automatic sentence segmentation of speech: a comparison of broadcast news and broadcast conversations. we investigate genre effects on the task of automatic sentence segmentation, focusing on two important domains - broadcast news (bn) and broadcast conversation (bc). we employ an hmm model based on textual and prosodic information and analyze differences in segmentation accuracy and feature usage between the two genres using both manual and automatic speech transcripts. experiments are evaluated using czech broadcast corpora annotated for sentence-like units (sus). prosodic features capture information about pause, duration, pitch, and energy patterns. textual knowledge sources include words, part-of-speech, and automatically induced classes. we also analyze effects of using additional textual data that is not annotated for sus. feature analysis reveals significant differences in both textual and prosodic feature usage patterns between the two genres. the analysis is important for building automatic understanding systems when limited matched-genre data are available, or for designing eventual genre-independent systems.
brain mri t1-map and t1-weighted image segmentation in a variational framework. in this paper we propose a constrained version of mumford-shah's[1] segmentationwith an information-theoretic point of view[2] in order to devise a systematic procedure to segment brain mri data for two modalities of parametric t1-map and t1-weighted images in both 2-d and 3-d settings. the incorporation of a tuning weight in particular adds a probabilistic flavor to our segmentation method, and makes the three-tissue segmentation possible. our method uses region based active contours which have proven to be robust. the method is validated by two real objects which were used to generate t1-maps and also by two simulated brains of t1-weighted data from the brainweb[3] public database.
a new stochastic estimator for tremor frequency tracking. an important parameter in analysis of physiological tremor is the diagnosis and study of neurological disorders. the instantaneous tremor frequency (itf) is an important parameter in tremor analysis. this paper proposes a novel stochastic filter, the multiple extended kalman filter (m-ekf), for tracking of itf from neural microelectrode recordings. the m-ekf mitigates degradations in filter performance resulting from a mismatch between assumed initial conditions and those of a particular realization of a stochastic system. specifically, the m-ekf is comprised of a bank of extended kalman filters (ekf), each initialized with different conditions, selected according to the unscented transform. the final estimate is a weighted average of the individual estimates provided by each ekf where the weights reflect how closely the assumed ekf initial conditions match those of the true system. the m-ekf is applied to a synthetic tremor model to display its superior performance to that of the ekf and the unscented kalman filter.
generating high performance pruned fft implementations. we derive a recursive general-radix pruned cooley-tukey fast fourier transform (fft) algorithm in kronecker product notation. the algorithm is compatible with vectorization and parallelization required on state-of-the-art multicore cpus. we include the pruned fft algorithm into the program generation system spiral, and automatically generate optimized implementations of the pruned fft for the intel core2duo multicore processor. experimental results show that using the pruned fft can indeed speed up the fastest available fft implementations by up to 30% when the problem size and the pattern of unused inputs and outputs are known in advance.
variational bayesian joint factor analysis for speaker verification. joint factor analysis (jfa) has been successfully applied to speaker verification tasks to tackle speaker and session variability. in the sense of bayesian statistics, it is beneficial to take account of the uncertainties in jfa to better characterize its speaker enrollment and verification processes, e.g. representing target speaker model by posteriori distribution of latent speaker factors and evaluating model likelihood by integrating over all latent factors. however, in a jfa model which has a large number of latent factors, it is computationally demanding to carry out these things in their exact form. in this paper, an alternative approach based on variational bayesian is developed to explore uncertainties in jfa in an approximate yet efficient way. in this method, fully correlated posteriori distribution is approximated by a variational distribution of factorial form to facilitate inference; and a tight lower bound on model likelihood is derived. experimental results on the 10sec4w-10sec4w task of the 2006 nist sre show that variational bayesian jfa could obtain better performance than jfa using point estimate.
unsupervised speaker adaptation for telephone call transcription. the use of the pc and internet for placing telephone calls will present new opportunities to capture vast amounts of un-transcribed speech for a particular speaker. this paper investigates how to best exploit this data for speaker-dependent speech recognition. supervised and unsupervised experiments in acoustic model and language model adaptation are presented. using one hour of automatically transcribed speech per speaker with a word error rate of 36.0%, unsupervised adaptation resulted in an absolute gain of 6.3%, equivalent to 70% of the gain from the supervised case, with additional adaptation data likely to yield further improvements. lm adaptation experiments suggested that although there seems to be a small degree of speaker idiolect, adaptation to the speaker alone, without considering the topic of the conversation, is in itself unlikely to improve transcription accuracy.
compressive sensing for sparsely excited speech signals. compressive sensing (cs) has been proposed for signals with sparsity in a linear transform domain. we explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. since the linear transform is signal dependent and unknown, unlike the standard cs formulation, a codebook of transfer functions is proposed in a matching pursuit (mp) framework for cs recovery. it is found that mp is efficient and effective to recover cs encoded speech as well as jointly estimate the linear model. moderate number of cs measurements and low order sparsity estimate will result in mp converge to the same linear transform as direct vq of the lp vector derived from the original signal. there is also high positive correlation between signal domain approximation and cs measurement domain approximation for a large variety of speech spectra.
an analytical approach to sound field reproduction with a movable sweet spot using circular distributions of loudspeakers. sound field reproduction methods like higher order ambisonics which are based on orthogonal expansions always introduce a limitation of the spatial bandwidth of the secondary source driving function. this spatial truncation creates a sweet spot in the center of the secondary source distribution. this spot, or rather area, is “sweet” both in terms of spatial aliasing artifacts as well as in terms of accuracy of the desired component of the reproduced wave field. the higher the temporal frequency of the reproduced wave field the smaller is the sweet spot. in this paper we show that the location sweet spot can be moved freely inside the secondary source distribution. the accuracy of the actual reproduced wave field is then significantly higher in the sweet spot than in the same region in the conventional approach.
model-based analysis of speech and audio signals for real-time processing based on time-varying lattice filters. in this paper, a time-varying analysis procedure based on lattice filters is proposed for real-time processing. the analysis procedure estimates the reflection coefficients from the signal frames successively in a way that the current frame is analyzed with respect to previous frames. for that purpose, a linear coefficient trajectory covering the current and previous frames is estimated under condition that the left-sided starting value of the trajectory is prescribed by analysis results of previous frames. the coefficients of the current frame can be determined from the trajectory at a particular point. analyses of speech signals show that the algorithm yields good spectral modeling simultaneously with a smooth time development.
multichannel nonnegative matrix factorization in convolutive mixtures. with application to blind audio source separation. we consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly under-determined convolutive mixture of source signals. each source is given a model inspired from nonnegative matrix factorization (nmf) with the itakura-saito divergence, which underlies a statistical model of superimposed gaussian components. we address estimation of the mixing and source parameters using two methods. the first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization algorithm. the second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from nmf methodology. our decomposition algorithms were applied to stereo music and assessed in terms of blind source separation performance.
accelerated 3d mri of vocal tract shaping using compressed sensing and parallel imaging. 3d mri of the upper airway has provided valuable insights into vocal tract shaping and data for the modeling of speech production. small movements of articulators can lead to large changes in the produced sound, therefore improving the resolution of these datasets, within the constraints of a sustained sound (6–12 seconds), is an important area for investigation. this paper provides the first application of compressed sensing (cs) with parallel imaging to high-resolution 3d upper airway mri. we use spatial finite difference as the sparsifying transform, and investigate the use of high-resolution phase information as a constraint during cs reconstruction. in a retrospective subsampling experiment with no sound production, 5x undersampling produced acceptable image quality when using phase-constrained cs reconstruction. the prospective use of this accelerated acquisition enabled 3d vocal-tract mri during sustained production of english /s/,/∫/,/i/,/r/ with 1.33×1.33×1.33-mm3 spatial resolution and 10-seconds of scan time.
lsh banding for large-scale retrieval with memory and recall constraints. locality sensitive hashing (lsh) is widely used for efficient retrieval of candidate matches in very large audio, video, and image systems. however, extremely large reference databases necessitate a guaranteed limit on the memory used by the table lookup itself, no matter how the entries crowd different parts of the signature space, a guarantee that lsh does not give. in this paper, we provide such guaranteed limits, primarily through the design of the lsh bands. when combined with data-adaptive bin splitting (needed on only 0.04% of the occupied bins) this approach provides the required guarantee on memory usage. at the same time, it avoids the reduced recall that more extensive use of bin splitting would give.
iterative target detection approach for through-the-wall radar imaging. we consider the problem of target detection in through-the-wall radar imaging when no a priori knowledge about the image statistics is available. an iterative approach which adapts itself to the unknown image statistics and thus allows for automatic target detection is presented. two variants, based on 2d median filtering and morphological operations, are described in details. the proposed detection schemes are tested using experimental data, considering the problem of 3d reconstruction of a scene hidden behind a concrete wall.
partial iterative equalization and channel decoding. this paper proposes a bit-selective iterative processing scheme for turbo equalization. we consider convolutionally coded and turbo coded transmissions over inter-symbol interference (isi) channels. the receiver starts with conventional iterative channel equalization and decoding. after each iteration, bit convergence status is measured via cross-entropy. short windows are then applied around unconverged bits and the subsequent detection/decoding iterates only within the windows. the rapid decrease in selected bits after each such iteration reduces computational complexity significantly, but performance is well maintained by utilizing the markov property in the trellis detection/decoding.
a differential motion estimation method for image interpolation in distributed video coding. motion estimation methods based on differential techniques proved to be very useful in the context of video analysis, but have a limited employment in classical video compression because, though accurate, the dense motion vector field they produce requires too much coding resource and computational effort. on the contrary, this kind of algorithm could be useful in the framework of distributed video coding (dvc). in this paper we propose a differential motion estimation algorithm which can run at the decoder in a dvc scheme, without requiring any increase in coding rate. this algorithm allows a performance improvement in image interpolation with respect to state-of-the-art algorithms.
fault detection combining interacting multiple model and multiple solution separation for aviation satellite navigation system. in civil aviation applications, satellite failures yield unacceptable positioning errors when using the global positioning system (gps). to ensure the user security, the navigation system has to fulfill stringent performance requirements. thus, detecting and excluding the faulty gps measurements is necessary prior to estimating the mobile location. classical fault detection algorithms based on kalman filters (kf) are sensitive to the choice of an appropriate motion model for the mobile. to overcome this difficulty, we propose in this paper a new fault detection algorithm wherein the kf are replaced by multiple model algorithms. in this way, both the false alarm rate and the position mean square error are shown to be decreased.