waashk/dblp huggingface dataset:
on the eigendistribution of the steady-state error covariance matrix for the extended rls algorithm. in an earlier work [1], we used transform methods from the theory of random matrices to analytically compute the asymptotic eigendistribution of the error covariance matrix of the single-measurement rls filter. when we have a multiplicity of measurements, as happens in extended rls filtering, the analysis is much more complicated. in this paper we study the multiple measurement case and obtain a system of two coupled equations for the stieltjes transform of the asymptotic eigendistribution. numerical solutions of this system very well predict the actual asymptotic eigendistribution for systems with as low as n = 10 − 20 state dimensions.
bayesian feature enhancement using a mixture of unscented transformation for uncertainty decoding of noisy speech. a new parameter estimation method for the model-based feature enhancement (mbfe) is presented. the conventional mbfe uses the vector taylor series to calculate the parameters of non-linearly transformed distributions, though the linearization leads to a degraded performance. we use the unscented transformation to estimate the parameters, where a minimal number of samples propagated through the nonlinear transformation are used. by avoiding the linearization, the parameters are estimated more accurately. experimental results on aurora2 show that the proposed method reduces the word error rate by 8.48% relatively, while the computational cost is just modestly higher, compared with the conventional mbfe.
region-based weighted-norm approach to video super-resolution with adaptive regularization. we propose a super-resolution (sr) algorithm that takes into account inaccurate estimates of the registration parameters. when frames obey the assumed global motion model, these inaccurate estimates, along with the additive gaussian noise in the low-resolution image sequence, result in different noise level for each frame. however, in case of existence of local motion and/or occlusion, regions that have local motion and/or occlusion have different noise level. to cope with this problem, we propose to adaptively weight each region according to its reliability and the regularization parameter is simultaneously estimated for each region. the regions are generated by segmenting the reference frame using watershed segmentation. the experimental results using real video sequences show the effectiveness of the proposed algorithm compared to three state-of-the-art sr algorithms.
an iterative approach to monaural musical mixture de-soloing. in this article, we introduce a novel approach for monaural source separation with the specific aim to separate a polyphonic musical recording into two main sources: a main instrument (or melody) track and an accompaniment track. to that aim, we propose to model the power spectral densities (psds) of both contributions with a source/filter model for the main instrument while retaining a model emphasizing temporal repetitions of the musical background. we show that improved source separation performances can be obtained by a two-step estimation strategy where the model parameters are re-estimated in a second stage by adequately exploiting the main melody line estimated in a first stage. the experiments conducted on several monaural signal databases show that our system achieves state-of-the-art performances compared to other unsupervised source separation algorithms.
ica-based efficient blind dereverberation and echo cancellation method for barge-in-able robot audition. this paper describes a new method that allows “barge-in” in various environments for robot audition. “barge-in” means that a user begins to speak simultaneously while a robot is speaking. to achieve the function, we must deal with problems on blind dereverberation and echo cancellation at the same time. we adopt independent component analysis (ica) because it essentially provides a natural framework for these two problems. to deal with reverberation, we apply a multiple input/output inverse-filtering theorem-based model of observation to the frequency domain ica. the main problem is its high-computational cost of ica. we reduce the computational complexity to the linear order of reverberation time by using two techniques: 1) a separation model based on observed signal independence, and 2) enforced spatial sphering for preprocessing. the experimental results revealed that our method improved word correctness of reverberant speech by 10–20 points.
incorporating mask modelling for noise-robust automatic speech recognition. in this paper we investigate an incorporation of mask modelling into an hmm-based asr system. the mask model is estimated for each hmm state and mixture by using a separate viterbi-style training procedure and it expresses which regions of the spectrum are expected to be uncorrupted by noise for the hmm state. experimental evaluation is performed on noisy speech data from the aurora 2 database. significant performance improvements are achieved when the mask modelling is incorporated within the standard model and two models that had already compensated for the effect of the noise.
target tracking based network active queue management. active queue management (aqm) methods attempt to predict and control network router queue levels and provide feedback regarding network congestion to data sources through packet marking/ dropping. aqm methods have not employed statistical signal processing principles largely due to the requirement of low complexity. in this paper, we apply optimal filtering and target tracking methods to the design of aqm. in particular, we develop kalman filter based aqm which results in router queues with reduced queue level variance. to account for networks with more bursty traffic, we use interacting multiple models (imm) which similarly result in reduced queue variance in simulations with both long-term and bursty short-term traffic. in comparisons with other aqm methods, these low complexity target tracking-based aqm methods give a more constant queue length without any loss in source throughput.
consistent recovery of stimuli encoded with a neural ensemble. we consider the problem of reconstructing finite energy stimuli from a finite number of contiguous spikes. the reconstructed signal satisfies a consistency condition: when passed through the same neuron, it triggers the same spike train as the original stimulus. the recovered stimulus has to also minimize a quadratic smoothness criterion. we show that under these conditions, the problem of recovery has a unique solution and provide an explicit reconstruction algorithm for stimuli encoded with a population of integrate-and-fire neurons. we demonstrate that the quality of reconstruction improves as the size of the population increases. finally, we demonstrate the efficiency of our recovery method for an encoding circuit based on threshold spiking that arises in neuromorphic engineering.
fast dependent components for fmri analysis. canonical correlation analysis (cca) can be used to find correlating projections of two datasets with co-occurring samples. instead of correlation, we would typically want to find more general dependencies, measured by mutual information. variants of cca based on non-parametric estimation of mutual information have been proposed previously; they outperform traditional cca for non-gaussian data but require infeasible amounts of computation for already quite modest sample sizes. we introduce a novel variant that uses a semiparametric estimate leading to a considerably faster algorithm. we apply the method on searching for statistical dependencies between multi-sensory stimuli and functional magnetic resonance imaging (fmri) of brain activity- in contrast to using regression on either of them.
comparison of scoring methods used in speaker recognition with joint factor analysis. the aim of this paper is to compare different log-likelihood scoring methods, that different sites used in the latest state-of-the-art joint factor analysis (jfa) speaker recognition systems. the algorithms use various assumptions and have been derived from various approximations of the objective functions of jfa. we compare the techniques in terms of speed and performance. we show, that approximations of the true log-likelihood ratio (llr) may lead to significant speedup without any loss in performance.
tree configuration games for distributed stream mining systems. we consider the problem of configuring classifier trees in distributed stream mining system. the configuration involves selecting appropriate false-alarm detection tradeoffs for each classifier to minimize end-to-end penalty in terms of misclassification cost. we model this as a tree configuration game and design solutions, where individual classifiers select their operating points to maximize a local utility. we derive appropriate misclassification cost coefficients for intermediate classifiers, and determine the information that needs to be exchanged across classifiers, in order to successfully design the game. we analytically show that there is a unique pure strategy nash equilibrium in operating points, which guarantees a convergence of the proposed approach. we evaluate the performance of our algorithm on an application for sports scene classification, and compare against centralized solutions. we show that our algorithm results in better performance than the centralized solution on average. moreover, the algorithm approaches the optimal solution asymptotically with increasing number of actions per classifier.
log-likelihood ratio clipping in mimo-bicm systems: information geometric analysis and impact on system capacity. the clipping of log-likelihood ratios (llrs) in soft demodulators for multiple-input multiple-output (mimo) systems with bit-interleaved coded modulation (bicm) was recently observed to allow for enormous complexity savings. in this paper we first provide an information-geometric interpretation of llr clipping as information projection onto a log-convex manifold. then we study the system capacity of mimo-bicm systems that use llr clipping. our results show that strong llr clipping is possible without significant capacity loss. we finally propose an llr transformation scheme which is necessary for approaching capacity in case of strong clipping. the usefulness of this llr transformation is illustrated by numerical simulations for mimo-bicm systems employing low-density parity check (ldpc) codes.
towards source-filter based single sensor speech separation. we present a new source-filter based method to separate two speakers talking simultaneously at equal level mixed into a single sensor. first, the relation between the spectral whitened mixture and the speakers excitation signals is analyzed. therefore, a factorial hmm capturing also time dependencies is exploited. then, the estimated excitation signals are combined with best fitting vocal tract information taken from a trained dictionary. we report results on the database of cooke considering 108 speech mixtures. the average improvement of 2.9 db in sir for all data is lower but not significantly lower compared to the gaussian mixture method which relies on known pitch-tracks. although the performance is currently moderate we believe in this approach and its significance towards the development of speaker independent single sensor speech separation.
comparing maximum a posteriori vector quantization and gaussian mixture models in speaker verification. gaussian mixture model - universal background model (gmm-ubm) is a standard reference classifier in speaker verification. we have recently proposed a simplified model using vector quantization (vq-ubm). in this study, we extensively compare these two classifiers on nist 2005, 2006 and 2008 sre corpora, while having a standard discriminative classifier (glds-svm) as a reference point. we focus on parameter setting for n-top scoring, model order, and performance for different amounts of training data. the most interesting result, against a general belief, is that gmm-ubm yields better results for short segments whereas vq-ubm is good for long utterances. the results also suggest that maximum likelihood training of the ubm is sub-optimal, and hence, alternative ways to train the ubm should be considered.
sparse decomposition of two dimensional signals. in this paper, we consider sparse decomposition (sd) of two-dimensional (2d) signals on overcomplete dictionaries with separable atoms. although, this problem can be solved by converting it to the sd of one-dimensional (1d) signals, this approach requires a tremendous amount of memory and computational cost. moreover, the uniqueness constraint obtained by this approach is too restricted. then in the paper, we present an algorithm to be used directly for sparse decomposition of 2d signals on dictionaries with separable atoms. moreover, we will state another uniqueness constraint for this class of decomposition. our algorithm is obtained by modifying the smoothed l0 (sl0) algorithm, and hence we call it two-dimensional sl0 (2d-sl0).
state mapping for cross-language speaker adaptation in tts. cross-language speaker adaptation has many interesting applications, e.g. speech-to-speech translation. however, in cross-language speaker adaptation, a common phoneme set, assumed to be used by different speakers of the same language, does not exist any longer. instead, a nearest neighbor based phoneme mapping from one language to the other has been adopted. in this study, we used our recently proposed sub-phonemic hmm state mapping for cross-language adaptations. the sub-phonemic hmm states, due to their phonetic segment nature, tend to be more sharable across different languages than phonemes. kullback-leibler divergence, an information-theoretic measure, is chosen here to measure the similarity between given states in different languages. experimental results show that new state mapping outperforms the phoneme mapping baseline system in terms of three objective measures: log spectral distance, f0 adaptation error and f0 correlations. in comparing with intra-language adaptation, the cross-language result of the new algorithm is also fairly decent.
view-invariant tensor null-space representation for multiple motion trajectory retrieval and classification. in this paper, we propose a novel general framework for tensor based null space affine invariants, namely, tensor null space invariants (tnsi) with a linear classifier for high order data classification and retrieval. we first derive tnsi, which is perfectly invariant to multidimensional affine transformations due to camera motions for multiple motion trajectories in consecutive motion events. we subsequently propose an efficient classification and retrieval system relying on tnsi for archiving and searching motion events consisting of multiple motion trajectories. the simulation results demonstrate superior performance of the proposed systems.
filtering web text to match target genres. in language modeling for speech recognition, both the amount of training data and the match to the target task impact the goodness of the model, with the trade-off usually favoring more data. for conversational speech, having some genre-matched text is particularly important, but also hard to obtain. this paper proposes a new approach for genre detection and compares different alternatives for filtering web text for genre to improve language models for use in automatic transcription of broadcast conversations (talk shows).
mitigation of narrowband interference in differentially modulated communication systems. integration of rf/analog and digital into a single chip can result in the coupling of digital spurs in the analog front end of a communication systems and can severely degrade the receiver performance. in this paper, we propose a novel receiver architecture of a notch filter together with the feedback filter (fbf) of the decision feedback equalizer (dfe) to mitigate the impact of narrowband interference in differentially modulated communication systems without enhancing the inter-symbol interference (isi). the proposed solution can be adapted to receiver architectures that employ either coherent or non-coherent demodulation of differentially modulated systems. simulation results demonstrating the spur mitigation capability of the proposed solution is presented.
lattice-based optimization of sequence classification criteria for neural-network acoustic modeling. acoustic models used in hidden markov model/neural-network (hmm/nn) speech recognition systems are usually trained with a frame-based cross-entropy error criterion. in contrast, gaussian mixture hmm systems are discriminatively trained using sequence-based criteria, such as minimum phone error or maximum mutual information, that are more directly related to speech recognition accuracy. this paper demonstrates that neural-network acoustic models can be trained with sequence classification criteria using exactly the same lattice-based methods that have been developed for gaussian mixture hmms, and that using a sequence classification criterion in training leads to considerably better performance. a neural network acoustic model with 153k weights trained on 50 hours of broadcast news has a word error rate of 34.0% on the rt04 english broadcast news test set. when this model is trained with the state-level minimum bayes risk criterion, the rt04 word error rate is 27.7%.
perceptual quality based packet dropping for generalized video gop structures. our work builds a general visibility model of video packets which is applicable to various types of gop (group of pictures). the data used for analysis and building the model come from three subjective experiment sets with different encoding and decoding parameters on h.264 and mpeg-2 videos. we consider factors not only within a packet but also across its vicinity to account for possible temporal and spatial masking effects. this model can be useful for an intermediate router in a congested network to drop less visible packets to maintain overall video quality. experiments are done to compare our perceptual-quality-based packet dropping approach with existing drop-tail and hint-track-inspired cumulative-mse-based dropping methods. the result shows that our dropping method produces videos of higher perceptual quality for different network conditions and gop structures.
analysis-by-synthesis based switched transform domain split vq using gaussian mixture model. using analysis-by-synthesis (abs) approach, we develop a soft decision based switched vector quantization (vq) method for high quality and low complexity coding of wideband speech line spectral frequency (lsf) parameters. for each switching region, a low complexity transform domain split vq (trsvq) is designed. the overall rate-distortion (r/d) performance optimality of new switched quantizer is addressed in the gaussian mixture model (gmm) based parametric framework. in the abs approach, the reduction of quantization complexity is achieved through the use of nearest neighbor (nn) trsvqs and splitting the transform domain vector into higher number of subvectors. compared to the current lsf quantization methods, the new method is shown to provide competitive or better trade-off between r/d performance and complexity.
an evidence framework for bayesian learning of continuous-density hidden markov models. we present an evidence bayesian framework, which can learn both the prior distributions and posterior distributions from data, for continuous-density hidden markov models (cdhmm). the goal of this study is to build the regularized cdhmms to improve model generalization, and achieve desirable recognition performance for unknown test speech. under this framework, we develop an em iterative procedure to estimate the marginal distribution or the evidence function for exponential family distributions. by adopting the variational bayesian inference, we derive an empirical bayesian solution to cdhmm parameters and their hyperparameters. such a regularized cdhmm compensates the model uncertainty and the ill-posed conditions. compared with maximum likelihood (ml) or other bayesian approaches with heuristic hyperparameters, the proposed approach can utilize available data more effectively. the experiments on noisy speech recognition using aurora2 show that the proposed bayesian approach performs better than the baseline ml cdhmms especially with mismatched test data or limited training data.
separation of a subspace-sparse signal: algorithms and conditions. in this paper, we show how two classical sparse recovery algorithms, orthogonal matching pursuit and basis pursuit, can be naturally extended to recover block-sparse solutions for subspace-sparse signals. a subspace-sparse signal is sparse with respect to a set of subspaces, instead of atoms. by generalizing the notion of mutual incoherence to the set of subspaces, we show that all classical sufficient conditions remain exactly the same for these algorithms to work for subspace-sparse signals, in both noiseless and noisy cases. the sufficient conditions provided are easy to verify for large systems. we conduct simulations to compare the performance of the proposed algorithms.
spectrally rich phase distortion sound synthesis using an allpass filter. this paper examines a recently introduced technique for sound synthesis that uses a coefficient modulated allpass filter to cause phase modifications to its input signal. the intention in this work is to outline some of the properties of the coefficient modulated allpass filter and then to establish a connection between this new method and the older technique of phase distortion. results are presented to demonstrate how the allpass technique provides a spectrally richer output signal.
improvements on minimum covariance based spatial correlation transformation. in order to take advantage of the correlation information among different acoustic units in speech recognition, a novel approach named minimum covariance based spatial correlation transformation was proposed in [8], which achieves satisfactory performance. however, there are two issues of this approach which can still be improved, 1) the estimation of the transformation matrix; 2) the construction of the history data. in this paper, a new algorithm of estimating the transformation matrix and a new strategy of constructing history supervector are proposed. experimental results show that the improved approach achieves better performance than the original one.
signal characterization using generalized "time-phase derivatives" representation. natural signals are often characterized by a complex time-frequency behaviour. these signals exist in many different applications and systems from underwater acoustic to audio signals with sound attacks or electrical systems with partial discharges and commutation switches, for example. there is a huge number of time-frequency (tf) methods that aim to characterize these signals in terms of first phase derivative analysis (i.e instantaneous frequency law). recently, we introduced the time frequency distribution based on complex lag arguments. this distribution is able to reduce inner interferences terms which appear when studying non-linear tf components. it also offers access to an instantaneous law representation of any phase derivative order. in this paper, we use these two properties to study highly non-stationary signals as well as transient signals.