waashk/dblp huggingface dataset:
robust speech feature extraction based on gabor filtering and tensor factorization. in this paper, we investigate the speech feature extraction problem in the noisy environment. a novel approach based on gabor filtering and tensor factorization is proposed. from recent physiological and psychoacoustic experimental results, localized spectro-temporal features are essential for auditory perception. we employ 2d-gabor functions with different scales and directions to analyze the localized patches of power spectrogram, by which speech signal can be encoded as a general higher order tensor. then nonnegative tensor pca with sparse constraint is used to learn the projection matrices from multiple interrelated feature subspaces and extract the robust features. experimental results confirm that our proposed method can improve the speech recognition performance, especially in noisy environment, compared with traditional speech feature extraction methods.
nonsubsampled higher-density discrete wavelet transform for image denoising. recently, a new set of dyadic wavelet frames based on oversampled filter banks is introduced that provides a higher sampling in both time and frequency, compared to the usual dyadic wavelets. this transform [1] (called hddwt) is not shift-invariant; a feature which is desirable particularly for signal denoising. in this paper we propose a new transform, referred to as nonsubsampled hddwt (ns-hddwt), which is the shift-invariant version of hddwt. the ns-hddwt filter bank is built upon iterated nonsubsampled filter banks which are derived from the hddwt filter bank in a way that is similar to the a trous algorithm. we employ hddwt and ns-hddwt for decomposition of images by performing the separable filtering. the performance of both hddwt and ns-hddwt is assessed in image denoising. experimental results show that the performance of ns-hddwt is superior to that of hddwt, and in some cases ns-hddwt outperforms powerful wavelet-based image denoising methods.
joint map adaptation of feature transformation and gaussian mixture model for speaker recognition. this paper extends our previous work on feature transformation-based support vector machines for speaker recognition by proposing a joint map adaptation of feature transformation (ft) and gaussian mixture models (gmm) parameters. in the new approach, the prior probability density functions (pdfs) of ft and gmm parameters are jointly estimated using the background data under the maximum likelihood criteria. in this way, we derive a generic prior gmm that is more compact than the universal background model due to the reduction of speaker variations. with the prior pdfs, we construct a supervector to characterize a speaker using ft and gmm parameters. we conducted experiments on nist 2006 speaker recognition evaluation (sre06) data set. the results validated the effectiveness of the joint map adaptation approach.
optimal distributed detection of multiple hypotheses using blind algorithm. in a parallel distributed detection in order to design the optimal fusion rule, the fusion center needs to have perfect knowledge of the performance of the local detectors as well as the prior probabilities of the hypotheses. such knowledge is not available in most practical cases. in this paper, we propose a blind technique for the m-ary distributed detection problem. we derive the probability mass function of the local decisions and use this result to develop maximum likelihood estimates of unknown parameters. we also derive analytically the overall detection performance for both binary and m-ary distributed detection and discuss the difference of the overall detection performance obtained using the estimated values of unknown parameters and their true values. finally, we demonstrate the applicability of our results through numerical examples.
perceptual time varying linear prediction model for speech applications. a new perceptual time varying model for non-stationary analysis of speech signals is presented. some researches have already shown that the time varying linear prediction coding (tvlpc) model that was applied to speech signals increases the recognition performance of automatic speech recognition (asr) systems. this improvement has been achieved due to the incorporation of the speech dynamics information in the model. another work, perceptual linear prediction (plp) analysis of speech, has shown that a modified estimation of the auto correlation function (acf) of stationary speech frame yields major improvement to the recognition rate. the presented model, perceptual time varying linear prediction (ptvlp) analysis of speech, adopts the perceptual concepts, of how to estimate the acf, into the tvlpc model. this research shows that the proposed ptvlp model is more accurate, robust to noise and achieves better recognition rates than plp and tvlpc over wide snr range.
self-optimizing scheme for active noise and vibration control. this paper presents a new approach to rejection of sinusoidal disturbances acting at the output of a discrete-time complex-valued linear stable plant (e.g. acoustic channel) with unknown and possibly time-varying dynamics. it is assumed that the instantaneous frequency of the sinusoidal disturbance may be slowly varying with time and that the output signal is contaminated with wideband measurement noise. it is not assumed that a reference signal, correlated with the disturbance, is available. the proposed disturbance rejection algorithm automatically adjusts its adaptation gains to the rate of system and/or disturbance variation.
novel similarity invariant for space curves using turning angles and its application to object recognition. we present a new similarity invariant signature for space curves. this signature is based on the information contained in the turning angles of both the tangent and the binormal vectors at each point on the curve. for an accurate comparison of these signatures, we define a riemannian metric on the space of the invariant. we show through relevant examples that, unlike classical invariants, the one we define in this paper enjoys multiple important properties at the same time, namely, a high discrimination level, independence of any reference point, uniqueness property, as well as a good preservation of the correspondence between curves. moreover, we illustrate how to match 3d objects by extracting and comparing the invariant signatures of their curved skeletons.
an improved signal-selective direction finding algorithm using second-order cyclic statistics. a new signal-selective direction finding algorithm which exploits the property of the cyclostationarity of incoming signals is proposed. after dimensionality reducing by projecting the observed array data onto the signal subspace, the array manifold matrix is identified by the simultaneous diagonalization structure of the matrix pencil consisting of the cyclic correlation matrix and the cyclic conjugate correlation matrix. then the direction-of-arrivals (doas) are obtained from the phase-differences of the estimated array manifold matrix. simulation results demonstrate that the proposed algorithm is superior to the cyclic-music and cyclic-esprit in terms of the root mean squares errors (rmses) of the doa estimates.
relative pitch estimation of multiple instruments. we present an algorithm based on probabilistic latent component analysis and employ it for relative pitch estimation of multiple instruments in polyphonic music. a multilayered positive deconvolution is performed concurrently on mixture constant-q transforms to obtain a relative pitch track and timbral signature for each instrument. initial experimental results on mixtures of two instruments are quite promising and show high levels of accuracy.
intensity vector direction exploitation for exhaustive blind source separation of convolutive mixtures. this article presents a technique that uses the intensity vector direction exploitation (ivde) method for exhaustive separation of convolutive mixtures. while only a four-element compact sensor array is used, multiple channels for all possible source directions are produced by exhaustive separation. singular value decomposition (svd) is then applied to determine the signal subspace and the directions of the local maxima of the signal energy. this information is then used to select the channels containing the individual sources. while the original ivde method requires the prior knowledge of the directions of sources for separation, the present method eliminates this need and achieves fully blind separation. performing svd at a post-processing stage also improves the sound quality. the method has been tested for convolutive mixtures of up to four sources and typical separation performances are given.
a phonetic feature based lattice rescoring approach to lvcsr. large vocabulary continuous speech recognition (lvcsr) systems decode the input speech using diverse information sources, such as acoustic, lexical, and linguistic. although most of the unreliable hypotheses are pruned during the recognition process, current state-of-the-art systems often make errors that are “unreasonable” for human listeners. several studies have shown that a proper integration of acoustic-phonetic information can be beneficial to reducing such errors. we have previously shown that high-accuracy phone recognition can be achieved if a bank of speech attribute detectors is used to compute a confidence score describing attribute activation levels that the current frame exhibits. in those experiments, the phone recognition system did not rely on the language model to follow their word sequence constraints, and the vocabulary was small. in this work, we extend our approach to lvcsr by introducing a second recognition step during which additional information not directly used during conventional log-likelihood based decoding is introduced. experimental results show promising performance.
constrained optimisation of 3d polygonal mesh watermarking by quadratic programming. in this paper, we propose a blind and robust watermarking method for 3d polygonal meshes by minimising the mean square error between the original mesh and the watermarked mesh under several constraints. we have formulated the problem of assigning distortions to points in a 3d mesh to a quadratic programming problem, so it can be solved reliably and efficiently. comparing with similar approaches in [1], experiments indicate the advantages of our method in resisting gaussian noise.
inferring functional cortical networks from spike train ensembles using dynamic bayesian networks. a fundamental goal in systems neuroscience is to infer the functional connectivity among neuronal elements coordinating information processing in the brain. in this work, we investigate the applicability of dynamic bayesian networks (dbn) in inferring the structure of cortical networks from the observed spike trains. dbns have unique features that make them capable of detecting causal relationships between spike trains such as modeling time-dependent relationships, detecting non-linear interactions and inferring connectivity between neurons from the observed ensemble activity. a probabilistic point process model was used to assess the performance under systematic variations of the model parameters. results demonstrate the utility of dbn in inferring functional connectivity in cortical network models.
a novel fusion-based method for expression-invariant gender classification. in this paper, we propose a novel fusion-based gender classification method that is able to compensate for facial expression even when training samples contain only neutral expression. we perform experimental investigation to evaluate the significance of different facial regions in the task of gender classification. three most significant regions are used in our fusion-based method. the classification is performed by using support vector machines based on the features extracted using two-dimension principal component analysis. experiments show that our fusion-based method is able to compensate for facial expressions and obtained the highest correct classification rate of 95.33%.
detection of sparse signals under finite-alphabet constraints. in this paper, we solve the problem of detecting the entries of a sparse finite-alphabet signal from a limited amount of data, for instance obtained by compressive sampling. while existing methods either rely on the sparsity property, the finite-alphabet property, or none of those properties to solve the under-determined system of linear equations, we capitalize on both the sparsity and the finite-alphabet features of the signal. the problem is first formulated in a bayesian framework to incorporate the prior knowledge of sparsity, which is then shown to be solvable using sphere decoding (sd) or semi-definite relaxation (sdr) for efficient boolean programming. a few toy simulations show how our method can outperform existing works.
frame bounds estimation of frequency warping operators. in this work we provide an accurate analytical estimation of the frame bounds for frequency warping operators of arbitrary shaped non-smooth warping maps. we deal with both the nonuniform fourier transform approximation and the aliasing suppressed form of the frequency warping operator. the estimation procedure is obtained by using an analytical model of the aliasing operators which has been previously introduced. the provided estimations can be used as design formulas for the parameters, such as degree of smoothness and redundancy, involved in the definition of the frequency warping operators.
space kernel analysis. in this paper, we propose a novel nonparametric modeling technique, namely space kernel analysis (ska), as a result of the definition of the space kernel. we analyze the uncertainty of ska and show that ska is subjected to the bias/variance dilemma. nevertheless, we demonstrate that, by a proper choice of the space kernel matrix, ska is able to balance between the robustness and accuracy and hence outperforms other kernel-based learning methods. the cost function of ska is derived, and it proves that ska minimizes the weighted least squared cost function whose weight matrix is diagonal and determined by the space kernel matrix. the parallels between ska and several other nonparametric modeling techniques are examined. study shows that the traditional kernel regression, general regression neural network, similarity based modeling and radial basis function network are examples of ska with specified space kernel matrices.
nonlinear distributed source-channel coding over orthogonal additive white gaussian noise channels. the problem of designing simple and energy-efficient nonlinear distributed source-channel codes is considered. by demonstrating similarities between this problem and the problem of bandwidth expansion, a structure for source-channel codes is presented and analyzed. based on this analysis an understanding about desirable properties for such a system is gained and used to produce an explicit source-channel code which is then analyzed and simulated. it is shown that the code has a substantial gain compared to a linear source-channel code.
nonlinear filtering for continuous-time systems using the linear fractional transformation model. in this paper, we propose bayesian filtering technique for continuous-time dynamical models with sampled-data measurements using the linear fractional transformation (lft) model which transforms the nonlinear state space model into an exact equivalent linear model with a simple nonlinear feedback loop. the linear model is amenable to euler discretization. simulation results demonstrate that the proposed filtering technique gives better approximation and tracking performance than the unscented kalman filter (ukf) which diverges for highly nonlinear problems.
jitter compensation in sampling via polynomial least squares estimation. sampling error due to jitter, or noise in the sample times, affects the precision of analog-to-digital converters in a significant, nonlinear fashion. in this paper, a polynomial least squares (pls) estimator is derived for an observation model incorporating both independent jitter and additive noise, as an alternative to the linear least squares (lls) estimator. after deriving this estimator, its implementation is discussed, and it is simulated using matlab. in simulations, the pls estimator is shown to improve the mean squared error performance by up to 30 percent versus the optimal linear estimator.
transforms for the motion compensation residual. the discrete-cosine-transform (dct) is the most widely used transform in image and video compression. its use in image compression is often justified by the notion that it is the statistically optimal transform for first-order markov signals, which have been used to model images. in standard video codecs, the motion-compensation residual (mc-residual) is also compressed with the dct. the mc-residual may, however, possess different characteristics from an image. hence, the question that arises is if other transforms can be developed that can perform better on the mc-residual than the dct. inspired by recent research on direction-adaptive image transforms, we provide an adaptive auto-covariance characterization for the mc-residual that shows some statistical differences between the mc-residual and the image. based on this characterization, we propose a set of block transforms. experimental results indicate that these transforms can improve the compression efficiency of the mc-residual.
kernel-based nonlinear independent component analysis for underdetermined blind source separation. in this paper we propose a new unsupervised training method for nonlinear spatial filter using a new independent component analysis based on kernel infomax. the nonlinearity of the spatial filter used in this paper is equivalent to the integration of beamforming and spectral subtraction, and the whole structure is optimized by independent component analysis in the reproducing kernel hilbert space. the optimized filter is shown to be capable of achieving better quality output than the conventional method based on time-frequency binary masking.
a differential cooperative transmission scheme with low rate feedback. the use of cooperative schemes in wireless networks has recently attracted much attention in scenarios where application of multiple-antenna systems is impractical. in such scenarios, the requirement of having full channel state information (csi) at the receiver side can be relaxed by using differential distributed (dd) transmission schemes. however, in the dd schemes proposed so far, the decoding complexity as well as the delay requirements increase with the number of relays. in this paper, we propose a low-rate feedback-based dd approach (with one-bit feedback per relay) that enjoys full diversity, linear maximum likelihood (ml) decoding complexity, and unrestrictive delay requirements. in addition, the proposed feedback scheme does not require any csi knowledge at the receiver, and its implementation is simple. computer simulations demonstrate substantial performance improvements of the proposed techniques as compared to several popular cooperative transmission schemes.
an audio indexing system for election video material. in the 2008 presidential election race in the united states, the prospective candidates made extensive use of youtube to post video material. we developed a scalable system that transcribes this material and makes the content searchable (by indexing the meta-data and transcripts of the videos) and allows the user to navigate through the video material based on content. the system is available as an igoogle gadget1 as well as a labs product (labs.google.com/gaudi). given the large exposure, special emphasis was put on the scalability and reliability of the system. this paper describes the design and implementation of this system.
bandwidth extension for china avs-m standard. we proposed a new frequency domain bandwidth extension (bwe) technology. in the new technology, fft based frequency domain gain shaping combined with linear prediction coding (lpc) based spectral envelope shaping is used for generating high frequency signals. to preserve the amount of noise component in the reconstructed band, gain reduction controlled by spectrum flatness measurement (sfm) is employed. subjective testing results show that the presented technology exhibits a comparable performance compared to 3gpp amr-wb+ with the same bit-rate in the framework of audio video coding of china standard (avs) part 10 - mobile speech and audio codec. this technology has been formally adopted as the artificial high band coding module in avs p10.
distributed peer-to-peer beamforming for multiuser relay networks. a computationally efficient distributed beamforming technique for multi-user relay networks is developed. the channel state information is assumed to be known at the relays or destinations, and the total relay transmitted power is minimized subject to the destination quality-of-service constraints. it is shown that this problem can be approximately converted to a convex second-order cone programming form. as a result, the proposed network beamforming technique offers a substantially reduced computational complexity than earlier state-of-the-art techniques that are based on semidefinite relaxation.
applying discretized articulatory knowledge to dysarthric speech. this paper applies two dynamic bayes networks that include theoretical and measured kinematic features of the vocal tract, respectively, to the task of labeling phoneme sequences in unsegmented dysarthric speech. speaker dependent and adaptive versions of these models are compared against two acoustic-only baselines, namely a hidden markov model and a latent dynamic conditional random field. both theoretical and kinematic models of the vocal tract perform admirably on speaker-dependent speech, and we show that the statistics of the latter are not necessarily transferable between speakers during adaptation.
novel approaches to parallel h.264 decoder on symmetric multicore systems. novel approaches to parallel h.264 decoder for symmetric multicore processors are presented. the basic partitioning of the decoder is coarse-grained and hybrid method of the data partitioning and functional partitioning. we investigate the performance bottleneck of the parallelized decoder, and propose two new approaches, software memory throttling and fair load balancing. the software memory throttling limits the number of cores involved in the parallel motion compensation to achieve better speedup and power-saving. the fair load balancing for deblocking filter reduces load imbalance caused by the conventional static partitioning method. from the evaluation on two different symmetric multicore platforms, proposed approaches show up to 24% of speedup when there is much bandwidth contention.
blind noise variance estimation for ofdma signals. we present two new noise variance estimation methods for ofdma signals transmitted through an unknown multipath fading channel. we focus on blind estimation as it does not require any pilot sequences and is therefore applicable to contexts, such as cognitive radio for instance, where little prior signal knowledge is available. the two estimators are respectively based on the time-frequency sparsity of ofdma signals and on the redundancy induced by the cyclic prefix. numerical simulations compare the performance of the two algorithms and highlight their complementarity.