waashk/dblp huggingface dataset:
iterative inverse filtering by lattice filters for time-varying analysis and synthesis of speech. in this contribution, an analysis procedure is proposed for time-varying analysis and synthesis of speech based on lattice filters. the estimation is performed by an iterative inverse filtering approach exploiting analytical suboptimal solutions. starting from an initial configuration of coefficients, the procedure estimates a continuous piece-wise linear trajectory in terms of reflection coefficients. in this way, smooth trajectories can be estimated which have additionally a high time resolution. one advantage of this analysis technique is that the coefficient trajectories are estimated in the same way as they are used for the synthesis. examples of synthesized speech signals show that the proposed algorithm suppresses artifacts which are caused by the use of time-invariant estimation procedures.
solutions and comparison of maximum likelihood and full-least-squares estimations for circle fitting. the fitting of a number of noisy data points with a circle has found numerous applications in image processing and pattern recognition. this paper examines two methods to estimate the circle parameters: the maximum likelihood (ml) method and the full-least-squares (fls) method. the ml method is based on the noisy model from the data while the fls method minimizes the geometric distance square. we first provide the iterative solutions of them using taylor-series linearization approach. we then show analytically that fls does not yield the ml solution. this is in contrast to previous study that the fls method gives the same solution as ml. fls method approximates the ml estimation only if the noise power is much less than the circle radius square. simulations are included to support the theoretical development.
combining mixture weight pruning and quantization for small-footprint speech recognition. semi-continuous acoustic models, where the output distributions for all hidden markov model states share a common codebook of gaussian density functions, are a well-known and proven technique for reducing computation in automatic speech recognition. however, the size of the parameter files, and thus their memory footprint at runtime, can be very large. we demonstrate how non-linear quantization can be combined with a mixture weight distribution pruning technique to halve the size of the models with minimal performance overhead and no increase in error rate.
reconstruction of isometrically deformable flat surfaces in 3d from multiple camera images. this paper deals with the reconstruction of smooth, flexible, isometrically embedded flat surfaces in 3d, such as a sheet of paper or a flag waving in the wind, from a set of 2d projected observations such as camera images. to solve the problem, a set of matched features of the waving object at different poses is needed, which are then applied to the reconstruction algorithm here described. the complete algorithm consists of 2 phases, the first obtaining an initial approximation from local features, the second uses this result to iterate a global cost function, trying to achieve a better estimate. to validate the algorithm, synthetic data with noise is generated, reconstructed and compared to ground truth data. also, a second experiment consisting of real images of a sheet of paper is shown.
forward adaptive klt coding. a framework for implementing the forward adaptive karhunen-loève transform (faklt) is described. unlike backward adaptive methods, faklt computes transform coefficients using basis vectors derived from the most recent signal frame. as a result, it exhibits improved energy compaction compared to the backward adaptive klt. the method encodes only the klt coefficients and a small amount of side information, the klt basis vectors (eigenvectors) are not encoded. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
partial update pnlms algorithm for network echo cancellation. the proportionate normalized least mean square (pnlms) algorithm has been proposed for network echo cancellation to take advantages of the sparseness of the echo path impulse responses. the pnlms algorithm has fast initial convergence but slows down dramatically after the initial period. in this paper, a novel algorithm to combine the pnlms algorithm with the technique of partial update is proposed. simulation results show that the proposed algorithm can achieve faster overall convergence with less computation.
robust two-channel tdoa estimation for multiple speaker localization by using recursive ica and a state coherence transform. a novel method is presented for a robust two channel multiple time difference of arrival (tdoa) estimation for multispeaker localization which can provide satisfactory performance even in highly reverberant environment. the method is based on a recursive frequency-domain independent component analysis (ica) and on a novel state coherence transform (sct). exploiting the phase coherence of the demixing matrices obtained in the ica stage the sct is able to generate envelopes with clear peaks in the corresponding maximum-likelihood tdoas. the sct envelopes are computed independently in each time-block and accurate multiple tdoas are estimated by means of a time-frequency sparse representation of the sources. the method has been applied to real data obtained by recording many sources in a room with a reverberation time of 700ms. experimental results show that an accurate localization of 7 closely-spaced sources is possibile given only few seconds of data even in the case of low snr. experiments also show the advantage of using the proposed solution rather than the well-known gcc-phat.
sparse variable pca using a steepest descent on a grassman manifold. recently there has developed considerable interest in using sparseness with pca. almost all previous methods concentrate on zeroing out some loadings. here we develop a new approach which zeros out whole variables automatically. we formulate a vector l1 penalized pca criterion and optimize it by steepest descent along geodesic on a grassman manifold. this ensures that each step obeys pca orthogonality as well as an invariance property of the criterion. we show in simulations that it outperforms a previous svpca algorithm and apply it to a real high dimensional functional magnetic resonance imaging (fmri) data.
design of oversampled dft modulated filter banks optimized for acoustic echo cancellation. this paper describes a method for designing oversampled dft filter banks (fb) optimized for subband acoustic echo cancellation (aec). for this application, the design requirements typically are good echo cancellation quality, low delay, small reconstruction error, and low computation complexity. our method explicitly includes a model for echo return loss enhancement (erle) as part of the optimization criteria. convergence of the high-dimensional, nonlinear optimization problem is facilitated by decorrelating the prototype filter impulse response via a discreet cosine transform (dct), discarding many insignificant coefficients and thus reducing the dimensionality of the search. the experimental results demonstrate the effectiveness of our design and the effects on the performance of aec, with elre improvements on the order of 3 db or better. the method is flexible and could also be extended to other application domains.
spatial multizone soundfield reproduction. spatial multizone soundfield reproduction is a difficult problem, which has many potential applications. this paper provides a framework to recreate 2d spatial multizone soundfields using an array of loudspeakers. we derive the desired global soundfield by translating individual desired soundfields to a single global co-ordinate system and applying appropriate angular window functions. we reveal some of the fundamental limits of 2d multizone soundfield reproduction. we show that the ability of multizone reproduction is dependent on (i) maximum radius of multizones, (ii) window length (size, and nature), and (iii) radial distance to the furthermost zone. we illustrate the framework by designing and simulating a two dimensional two zone soundfield.
reconciliation of human and machine speech recognition performance. this paper focuses on resolving a number of issues that appear when the performance of human speech recognition is compared to that of automatic speech recognition. in particular human experimental data suggest that the resulting error is a product of the individual streams. on the other hand, bayesian combination requires a multiplication of the estimates of prior probabilities and likelihoods. we show that, in principle, there is no discrepancy. the product of errors is a performance measure and human and machine performance may be consistent with this empirically established regularity. the product of probabilities is step in an algorithm to achieve the performance that may or may not be consistent with the product of errors. the main problem is that most of prior discussions failed to distinguish the performance measures from the estimates of the parameters used in the algorithm.
covert timing channels codes for communication over interactive traffic. this paper presents the first practical perfectly-secure steganography codes for covert communication via packet timings across interactive traffic relayed over network queuing systems. it has recently been shown that sparse-graph linear codes followed by shaping techniques, combined with message-passing decoding, can enable practical timing channel codes with low symbol error rates near the information capacity of the famous “bits through queues” channel. inspired by this new class of codes, we use an alternative shaping technique that employs random dithers and construct provably secure steganographic codes for communication using packet timings in interactive traffic. to validate the perfect secrecy of our steganographic codes, we model interactive traffic as a two-state markov modulated poisson process (mmpp) and show its goodness-of-fit.
using the scaling ambiguity for filter shortening in convolutive blind source separation. in this paper, we propose to use the scaling ambiguity of convolutive blind source separation for shortening the unmixing filters. an often used approach for separating convolutive mixtures is the transformation to the time-frequency domain where an instantaneous ica algorithm can be applied for each frequency separately. this approach leads to the so called permutation and scaling ambiguity. while different methods for the permutation problem have been widely studied, the solution for the scaling problem is usually based on the minimal distortion principle. we propose an alternative approach that allows the unmixing filters to be as short as possible. shorter unmixing filters will suffer less from circular-convolution effects that are inherent to unmixing approaches based on bin-wise ica followed by permutation and scaling correction. the results for the new algorithm will be shown on a real-world example.
focusing-based approach for wide-band source localization in near-field. a wide-band near-field source localization method is presented in this paper. based on a pre-estimated source location, we form a focusing matrix which is able to compensate the wavefront distortion (with respect to far-field wavefront) due to near-field propagation and frequency dependent phase shift simultaneously. the focused covariance matrix has been proved to have a partially far-field narrow band structure, which allows us to estimate the bearings of the sources by the well studied far-field doa estimators. the range estimation is then carried out via peak searching of the 1d music spectral with the estimated bearings. the performance of the algorithm is tested by simulations.
robust detection of a set of outliers for image changes based on rerunning the regression. by comparing two images, which are captured with the same scene at different times, we can detect the image changes due to moving objects. to reduce the influence from the different intensity properties of the images, an intensity compensation scheme, which is based on the polynomial regression model, is employed. for an accurate detection of outliers alleviating the influence from a set of outliers, a simple technique that reruns the regression is employed. in this paper, the algorithm that iteratively reruns the regression is theoretically analyzed by observing the convergency of the estimates of the noise variance. using an empirical compensation constant for the estimate is also proposed. the compensation enables the detection algorithm robust to the choice of thresholds for selecting outliers. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
a low-complexity spectro-temporal based perceptual model. the use of psychoacoustical masking models for audio coding applications has been wide spread over the past decades. in such applications, it is typically assumed that the original input signal serves as a masker for the distortions that are introduced by the lossy coding method that is used. up to now, these masking models are mostly based on spectral masking. in this paper, we propose a new perceptual model for audio and speech processing algorithms based on spectro-temporal masking. a sophisticated perceptual model is simplified, such that the eventual distortion measure can be written as a frequency-weighted l2-norm. this yields the same computational complexity as conventional spectral-based methods, but with the preservation of the temporal fine structure of the clean signal. it is shown that the new model can successfully avoid pre-echoes and can correctly predict masking curves for various maskers.
glottal closure instant detection using lines of maximum amplitudes (loma) of thewavelet transform. the lines of maximum amplitude (loma) of the wavelet transform are used for glottal closure instant detection. following kadambe & al. (1992), the wavelet transform modulus maxima can be used for singularity detection. the loma method extends this idea. all the lines chaining maxima of a wavelet transform across scales are built. then a back-tracking procedure allows for selection of the optimal line for each pitch period, the top of which indicates the gci. the loma method is then evaluated by comparing its results to the dypsa (naylor & al.) algorithm, with the option of using inverse filtering as preprocessing. the loma method compares favorably to dypsa, particularly on accuracy. one of the advantage of the loma method is its ability to deal with variations in the glottal source parameters.
a framework for distributed multimedia stream mining systems using coalition-based foresighted strategies. in this paper, we propose a distributed solution to the problem of configuring classifier trees in distributed stream mining systems. the configuration involves selecting appropriate false-alarm detection tradeoffs for each classifier to minimize end-to-end penalty in terms of misclassification cost. in the proposed solution, individual classifiers select their operating points (i.e., actions) to maximize a local utility function. the utility may be purely local to the current classifier, corresponding to a myopic strategy, or may include the impact of the classifier actions on successive classifiers in the tree, corresponding to a foresighted strategy. we analytically show that actions determined by the foresighted strategies can improve the end-to-end performance of the classifier tree and derive an associated probability bound. we then evaluate our solutions on an application for hierarchical sports scene classification. by comparing centralized, myopic and foresighted solutions, we show that foresighted strategies result in better performance than myopic strategies, and also asymptotically approach the centralized optimal solution.
online blind source separation based on time-frequency sparseness. recently, blind source separation (bss) has been proposed to separate signals recorded by a microphone array in a reverberant environment. this paper deals with bss of a time-varying number of moving sources, which often occurs in practical situations. we develop two online algorithms based on time-frequency (tf) sparseness that are able to deal with moving sources: a block-online algorithm that estimates the number of sources and a gradient-based online algorithm with prespecified maximum number of sources. both algorithms are evaluated in simulations and real-world scenarios and show good separation performance.
affine invariant features and their application to speech recognition. this paper proposes a set of affine invariant features (aifs) for sequence data. the proposed aifs can be calculated directly from the sequence data, and their invariance to affine transformation is proved mathematically through algebraic calculation. we apply the aifs to speech recognition. since the vocal tract length (vtl) difference causes to frequency warping which can be approximated well by affine transform on cepstral features [1], the aifs of cepstral sequence provide robust features for vtl variations. we experimentally examine the invariance of aifs of speech signals, and apply aifs for japanese isolated word recognition. the experimental results show that the combination of aifs with mfcc or mfcc+δ can lead to higher recognition rates than mfcc or mfcc+δ only. especially in the mismatched experiments, the combination with aifs can reduce the error rates about 30% when compared to mfcc or mfcc+δ only. the aifs are expected to have other applications than speech recognition, since their invariance is general.
acoustic-based pitch-accent detection in speech: dependence on word identity and insensitivity to variations inword usage. past work has produced fairly accurate automatic pitch-accent detectors, but it has often been noted that the accent class of a word is highly dependent on word identity, with some words and word types usually being accented and others not. we argue that a good accent detector should not only have high overall accuracy, but also be able to distinguish between accented and unaccented variants of the same word. we report on experiments with several classifiers trained on a hand-labeled corpus, using a large set of acoustic features. results show that while the classifiers have a high overall accuracy, they perform disappointingly on words with atypical accent status or whose prior accent status is more uncertain. we further report on attempts to improve the performance on these sub-tasks via feature selection and engineering of the training set.
compressive sampling of pulse trains: spread the spectrum! in this paper we consider the problem of sampling far below the nyquist rate signals that are sparse linear superpositions of shifts of a known, potentially wide-band, pulse. this signal model is key for applications such as ultra wide band (uwb) communications or neural signal processing. following the recently proposed compressed sensing methodology, we study several acquisition strategies and show that the approximations recovered via ℓ1 minimization are greatly enhanced if one uses spread spectrum modulation prior to applying random fourier measurements. we complement our experiments with a discussion of possible hardware implementation of our technique.
single and multichannel sampling of bilevel polygons using exponential splines. in this paper we present a novel approach for sampling and reconstructing any k-sided convex and bilevel polygon with the use of exponential splines [1]. it will be shown that with k+1 projections we are able to perfectly reconstruct a k-sided bilevel polygon from its samples. we will also investigate the multichannel sampling scenario, consisting of a bank of e-spline filters, each with a different delay parameter compared to the reference signal. we show how by retrieving the delay parameters, we can symmetrically sample and reconstruct a given bilevel polygon using exponential splines.
sound source separation of moving speakers for robot audition. this paper addresses sound source separation and speech recognition for moving sound sources. real-world applications such as robots should cope with both moving and stationary sound sources. however, most studies assume only stationary sound sources. we introduce two key techniques to cope with moving sources, that is, adaptive step-size control (as) and optima controlled recursive average (ocra) to improve blind source separation. we implemented a real-time robot audition system with these techniques for our humanoid robot asimo with an 8ch microphone array by using hark which is our open-source software for robot audition. the performance of the system will be shown through sound source separation for moving sources and automatic speech recognition of separated speeches.
a portable usb-based microphone array device for robust speech recognition. we present a usb-based, highly directional, and portable microphone array device that delivers a crisp, clear and noise-reduced speech signal. this device consists of four linearly distributed microphone sensors and a filter-and-sum beamformer designed using broadband beam-forming algorithm. the device has a narrow acoustic beam pattern and identical frequency responses for almost all speech bands. in addition to beamforming, an adaptive noise reduction algorithm is used to further reduce the background noise. by utilizing both the spatial and temporal information, the snr of speech signals is improved and speech recognition performance in noisy environments is significantly improved as reported in our experiments.
adaptive distributed transforms for irregularly sampled wireless sensor networks. we develop energy-efficient, adaptive distributed transforms for data gathering in wireless sensor networks. in particular, we consider a class of unidirectional transforms that are computed as data is forwarded to the sink along a given routing tree and develop a tree-based karhunen-loàve transform (klt) that is optimal in that it achieves maximum data de-correlation among this class of transforms. as an alternative to this klt (which incurs communication overhead in order to learn second order data statistics), we propose a backward adaptive filter optimization algorithm for distributed wavelet transforms that i) achieves near optimal performance and ii) has no communication overhead in learning statistics.
robust face recognition with partially occluded images based on a single or a small number of training samples. this paper investigates the problem of face recognition with partially occluded images without assuming prior information about the distortion, and with only a single training image or a small number of training images for each class to be identified. a new approach is presented, which is an extension of our previous posterior union model. the new approach is formulated by using a similarity measure in place of the probability measure, thereby allowing the use of a single training image to represent a class. the new approach achieves improved robustness to partial occlusion by focusing the recognition mainly on the matched local regions, which are selected automatically subject to an optimality criterion to maximize the similarity of the correct class. two databases, xm2vts and ar, have been used to evaluate the new approach. the results indicate that the new system is able to perform as well as an oracle model for dealing with various simulated and realistic partial distortions/occlusions without requiring prior information.