waashk/dblp huggingface dataset:
single parameter optimization approach to the optimal power allocation of ofdm relaying system. in this paper, we investigate the power allocation optimization problem for dual-hop nonregenerative ofdm relaying networks, where an aggregate power constraint is imposed on all relays. we first formulate the model into a single-parameter optimization problem, in which we found the related functions are monotonically decreasing. then based on the monotonicity, we propose a bisection algorithm to solve the problem. we further illustrate that pairing of ofdm subcarriers will not provide performance gain when the aggregate power constraint is very large.
mixture of probabilistic linear regressions: a unified view of gmm-based mapping techiques. this paper introduces a model of mixture of probabilistic linear regressions (mplr) to learn a mapping function between two feature spaces. the mplr consists of weighted combination of several probabilistic linear regressions, whose parameters are estimated by using matrix calculation. the mixture nature of mplr allows it to model nonlinear transformation. the formulation of mplr is general and independent of the types of the density models used. two well-known gmm-based mapping methods for voice conversion [1, 2] can be regarded as special cases of mplr. this unified view not only provides insights to the gmm-based mapping techniques, but also indicates methods to improve them. compared to [1], our formulation of mplr avoids solving complex linear equations and yields a faster estimation of the transform parameters. as for [2], the mplr estimation provides a modified mapping function which overcomes an implicit problem in [2]-s mapping function. we carried out experiments to compare the mplr-based methods with the traditional gmm-based methods [1, 2] on a voice conversion task. the experimental results show that the mplr-based methods always have better performance in various parameter setups.
an efficient lsf quantization using dynamic bit allocation. the current paper is concerned with an effective method to quantize a spectrum envelope of a speech signal without having an inter-frame prediction. in this paper, we proposed a method referred to as dynamic bit allocation-split vector quantization (dba-svq). the main feature of this structure is that it makes use of the ordering property of line spectral frequencies (lsf) and exploits multiple codebooks, normalization and the dba technique. as a result, we can limit the dynamic range of lsf sub-vectors and allocate different numbers of bits in accordance with the range sizes to maximize the overall efficiency of quantization. the performance is compared with delta line spectral pairs (lsp) vq, which is used in evrc-b, demonstrating reduction in spectral distortion (sd).
cache-based integer motion/disparity estimation for quad-hd h.264/avc and hd multiview video coding. to provide more vivid perception, more and more advanced features, like the 4k×2k resolution and the multiview functionality, are emerging for tv. for a multiview video coding (mvc) encoder, motion and disparity estimation (me/de) take at least half the hardware requirement. to solve these challenges, a cache-based integer me/de algorithm is proposed. with a cache memory as the search window buffer, a predictor-centered me/de algorithm is presented. the search range can be reduced to ±16 pixels with less than 0.1db quality drop compared with full search algorithm. based on this algorithm, an integer me/de chip design is realized. it can reduce 82% on-chip sram and 39% system bandwidth. moreover, the search candidate requirement is also reduced by 79%. as the result, an me/de chip design for 4k×2k quad-hd h.264 and hdtv mvc is implemented.
music analysis with a bayesian dynamic model. a bayesian dynamic model is developed to model complex sequential data, with a focus on audio signals from music. the music is represented in terms of a sequence of discrete observations, and the sequence is modeled using a hidden markov model (hmm) with time-evolving parameters. the model imposes the belief that observations that are temporally proximate are more likely to be drawn from hmms with similar parameters, while also allowing for “innovation” associated with abrupt changes in the music texture. segmentation of a given musical piece is constituted via the model inference and the results are compared with other models and also to a conventional music-theoretic analysis.
towards unsupervised learning for automatic multi-class object detection in surveillance videos. object detection is a critical step in automated surveillance. a common approach to constructing object detectors consists of annotating large datasets and using them to train the detectors. however, due to inevitable limitations of a typical training data set, such supervised approach is unsuitable for building generic surveillance systems applicable to a wide variety of scenes and camera setups. in our previous work we proposed an unsupervised method for learning and detecting the dominant object class in a general dynamic scene observed by a static camera. in this paper, we investigate the possibilities to expand the applicability of this method to the problem of multiple dominant object classes. we propose an idea how to approach this expansion, and perform a proof-of-concept evaluation of this idea using a representative surveillance video sequence.
interpolating hidden markov model and its application to automatic instrument recognition. this paper proposes an interpolating extension to hidden markov models (hmms), which allows more accurate modeling of natural sounds sources. the model is able to produce observations from distributions which are interpolated between discrete hmm states. the model uses gaussian mixture state emission densities, and the interpolation is implemented by introducing interpolating states in which the mixture weights, means, and variances are interpolated from the discrete hmm state densities. we propose an algorithm extended from the baum-welch algorithm for estimating the parameters of the interpolating model. the model was evaluated in automatic instrument classification task, where it produced systematically better recognition accuracy than a baseline hmm recognition algorithm.
spectrum separation of magnetic resonance spectroscopy based on sparse representation. in this paper, a novel spectrum separation technique based on sparse representation is proposed to deal with magnetic resonance spectroscopy (mrs) quantification which is used to measure the levels of different metabolites in brain tissues. since a measured mr spectrum contains the spectra of numbers of metabolites and a baseline, the separation and quantification of them becomes difficult. a nonnegative pursuit algorithm based on regularized focuss algorithm is proposed here to decompose a measured spectrum with respect to an overcomplete dictionary. benefitting from the a priori knowledge, the dictionary is built by lorentzian and gaussian basis functions representing different metabolites and baseline. using this algorithm, not only the baseline is separated from the spectra of interest, but also the spectra of different metabolites are separated. the accuracy of quantification and the robustness are improved, from simulation data, compared with a commonly used estimation method [1]. the quantification on tumor metabolism with in vivo brain mr spectra is also demonstrated.
spoken language interpretation: on the use of dynamic bayesian networks for semantic composition. in the context of spoken language interpretation, this paper introduces a stochastic approach to infer and compose semantic structures. semantic frame structures are directly derived from word and basic concept sequences representing the users' utterances. a rule-based process provides a reference frame annotation of the speech training data. then dynamic bayesian networks are used to hypothesize frames from test data. the semantic frames used in this work are specialized on the task domain from the berkeley framenet set. experiments are reported on the french media dialog corpus. for all the data, the manual transcriptions and annotations at the word and concept levels are available. tests are performed under 3 different conditions raising in difficulty wrt the errors in the word and concept sequence inputs. three different stochastic models are compared and the results confirm the ability of the proposed probabilistic frameworks to carry out a reliable semantic frame annotation.
accelerating em by targeted aggressive double extrapolation. the expectation-maximization (em) algorithm is one of the most popular algorithms for parameter estimation from incomplete data, but its convergence can be slowfor some large-scale or complex problems. extrapolation methods can effectively accelerate em, but to ensure stability, the learning rate of extrapolation must be compromised. this paper describes the tj2aem method, a targeted extrapolation method that can extrapolate much more aggressively than competing methods without causing instability problems. we analyze its convergence properties and report experimental results.
a wavelet-based quadratic extension method for image deconvolution in the presence of poisson noise. iterative optimization algorithms such as the forward-backward and douglas-rachford algorithms have recently gained much popularity since they provide efficient solutions to a wide class of non-smooth convex minimization problems arising in signal/image recovery. however, when images are degraded by a convolution operator and a poisson noise, a particular attention must be paid to the associated minimization problem. to solve it, we propose a new optimization method which consists of two nested iterative steps. the effectiveness of the proposed method is demonstrated via numerical comparisons.
class-dependent and differential huffman coding of compressed feature parameters for distributed speech recognition. in this paper, we propose an entropy coding method for compressing quantized mel-frequency cepstral coefficients (mfccs) used for distributed speech recognition (dsr). in the european telecommunication standards institute (etsi) extended dsr standard, mfccs are compressed with additional parameters such as pitch and voicing class. the entropy of compressed mfccs in each analysis frame varies according to the voicing class of the frame, thereby enabling the design of different huffman trees for mfccs according to voicing class, referred to here as class-dependent huffman coding. in addition to the voicing class, the correlation in subvector-wise is utilized for huffman coding, which is called subvector-wise huffman coding. it is also explored that differential huffman coding can further enhance a coding gain against class-dependent huffman coding and subvector-wise huffman coding. based on the benefits above, hybrid types of huffman coding by combining class-dependent and subvector-wise with differential huffman coding are compared in this paper. subsequent experiments show that the average bitrate of subvector-wise differential huffman coding is measured at 33.93 bits/frame, whereas that of a traditional huffman coding which does not consider voicing class and encodes with a single huffman coding tree for all the subvectors is at 42.22 bits/frame.
manifold regularization for semi-supervised sequential learning. the sequential data flux in many time-series applications require that only a small fraction of the data are stored for future processing. furthermore, labels for these data are possibly sparse and they might show some biases. to support learning under such restrictive constraints, we combine manifold regularization with sequential learning under a semi-supervised learning scenario. the online learning mechanism integrates a regularization based on the data smoothness assumptions. we present a proof-of-concept for illustrative toy problems, and we apply the algorithm to a real-world sparse online classification task for music categories.
predictive power control and multiple-description coding for wireless sensor networks. we study state estimation via wireless sensor networks over fading channels affected by random packet loss. in the configuration examined, the sensors send their measurements to a single gateway, which decides upon the source coding scheme and the sensor transmitter power levels. the decision process is carried out on-line and adapts to changing channel conditions to achieve an optimal trade-off between estimation quality and sensor energy expenditure. in particular, if some channel conditions are poor, then the gateway commands the corresponding sensors to increase power levels and use multiple-description coding. simulations based on measured channel data illustrate that the proposed scheme gives excellent results.
steady-state performance analysis for adaptive filters with error nonlinearities. a unified approach to the steady-state mean square error (mse) and tracking performance analyses for real and complex adaptive filtes with error nonlinearities is developed. some general clofied-form analytical expressions for the steady-state performances are given. our analyses are based on taylor series expansion and and so-called complex brandwood-form series expansion (bse). under these general explicit expressions, some well-known adaptive filters can be viewed as special cases. in addition, the closed-form analytical expressions for the steady-state performance for real and complex least-mean p-power (lmp) algorithm with different choices of parameter p are also given. a mass of simulations show the accuration of our analyses.
data sampling based ensemble acoustic modelling. in this paper, we propose a novel technique of using cross validation (cv) data sampling to construct an ensemble of acoustic models for conversational speech recognition. we further propose using hierarchical gaussian mixture model (hgmm) and repartition training data to increase the ensemble size and diversity. the proposed methods are found to work well together for ensemble acoustic modeling. we also evaluated the quality of the ensemble acoustic models by using the measures of classification margin, average correct score and variance of correct score. we have found that the ensemble of acoustic models increases the margin and the average correct score, and reduces the variance. we compared the performance of our proposed method with a recently reported method of cv expectation maximization (cvem) for single acoustic models. our experimental results on a telemedicine automatic captioning task showed that the proposed ensemble acoustic modeling has led to significant improvements in word recognition accuracy.
a new nonparametric measure of conditional independence. in this paper we propose a new measure of conditional independence that is loosely based on measuring the l2 distance between the conditional joint and the product of the conditional marginal density functions. however, we propose to smooth the arguments prior to measuring the distance and use kernel density estimation to derive the estimator. we show that under suitable conditions the proposed smoothing does not affect the conditional independence but using proper smoothing function helps in choosing the bandwidth parameter robustly. we discuss the computational issues and propose an approximation to evaluate the estimator efficiently. we apply the proposed measure in different experiments to show its validity.
complex nmf: a new sparse representation for acoustic signals. this paper presents a new sparse representation for acoustic signals which is based on a mixing model defined in the complex-spectrum domain (where additivity holds), and allows us to extract recurrent patterns of magnitude spectra that underlie observed complex spectra and the phase estimates of constituent signals. an efficient iterative algorithm is derived, which reduces to the multiplicative update algorithm for non-negative matrix factorization developed by lee under a particular condition.
transceiver design with vector perturbation technique and iterative power loading. in this paper we consider the optimization of transceivers which use the nonlinear vector perturbation technique at the transmitter. since the perturbation vector can be almost totally removed at the receiver, the transmitter can use this extra freedom to reduce the transmitted power while maintaining the performance. the two cases considered in this paper are linear transceivers and transceivers with decision feedback (dfe). for both cases, efficient iterative power loading algorithms are developed to reduce the average bit error rate under the total transmitted power constraint. we present simulation results showing that the proposed technique performs better than the existing state-of-the-art uniform channel decomposition (ucd) system and the vector perturbation (vp) precoder.
high improvement of speaker identification and verification by combining mfcc and phase information. in conventional speaker recognition methods based on mfcc, phase information has been ignored. we proposed a method that integrated the phase information with mfcc on a speaker identification method, and a preliminary experiment was performed. in this paper, we propose a new modified feature parameter (that is, coordidates on an unit circle) obtained from the original phase information, and evaluated it by using speech database consisting of normal, fast and slow speaking modes. the speaker identification experiments were performed using ntt database which consists of sentences uttered by 35 japanese speakers (22 males and 13 females) on five sessions over ten months. each speaker uttered only 5 training utterances at a normal speaking mode (about 20 seconds in total). the proposed new phase information was more robust than the original phase information for all speaking modes. by integrating the new phase information with the mfcc, the speaker identification error rate was remarkably reduced for normal, fast and slow speaking rates in comparison with a standard mfcc-based method. in this paper, speaker verification experiments were also evaluated using the phase information. the experiments show that the phase information is also very useful for the speaker verification.
speech enhancement using minimum mean-square error estimation and a post-filter derived from vector quantization of clean speech. in this paper, a novel post-filtering method applied after the logstsa filter is proposed. since the post-filter is derived from vector quantization of clean speech database, it has an equivalent effect of imposing clean source spectral constraints on the enhanced speech. when combined with the logstsa filter, the additional filter can noticeably suppress residual artifacts by effectively lowering the residual white noise of decision-directed estimation as well as reducing the musical noise of maximum likelihood estimation. compared to the logstsa enhanced speech, the overall enhanced speech is able to raise the pesq score by nearly half a point.
performance analysis of recursive least moduli algorithm for fast convergent and robust adaptive filters. this paper derives a new adaptation algorithm named recursive least moduli (rlm) algorithm that combines least mean modulus (lmm) algorithm for complex-domain adaptive filters with recursive estimation of the inverse covariance matrix of the filter reference input. the rlm algorithm achieves significant improvement in the filter convergence speed of the lmm algorithm with a strongly correlated filter reference input, while it preserves robustness of the lmm algorithm against impulsive observation noise. analysis of the rlm algorithm is developed for calculating transient and steady-state behavior of the filter convergence. through experiment with simulations and theoretical calculations of the filter convergence for the rlm algorithm, we demonstrate its effectiveness in making adaptive filters fast convergent and robust in the presence of impulse noise. good agreement between the simulations and theory proves the validity of the analysis. 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
linear-time block noncoherent detection of psk. we propose a new algorithm for noncoherent sequence detection of m-ary phase-shift-keying (m-psk) symbols transmitted over a block fading channel. the algorithm is of complexity o(t), where t is the sequence length, and is therefore computationally superior to existing maximum-likelihood (ml) detectors of complexity o(t logt). our detector is based on a new approximation we propose to the noncoherent ml function. we show that by using this close approximation, the detection problem reduces to a nearest lattice point problem for the lattice an*, from which we derive our o(t) approach. simulation results are provided that show the difference in bit error rate is negligibly small for a wide range of signal-to-noise ratios.
phase-based alignment of two signals having partially overlapped spectra. this paper proposes a novel method for aligning two signals using the information contained in the overlapped band. in particular the proposed method aligns two signals by compensating both time-delay and phase-offset in the second signal using the estimated gradient of phase difference in the overlapped band. compared with other conventional methods, this method can align two signals without requiring a pilot tone or additional hardware. the proposed method was experimentally validated using rf pulses.
multidimensional unitary tensor-esprit for non-circular sources. recently, many authors have shown that high-resolution parameter estimation schemes can be significantly improved if the sources are non-circular. for example, enhanced versions of root music and standard esprit for non-circular sources as well as the entirely real-valued nc unitary esprit algorithm have been proposed.
polyphase interpretation of empirical image interpolation. we observe several characteristics of empirical image interpolating algorithms and contribute four novel concepts and claims. first, we interpret well-known classification-based filtering algorithms in terms of their polyphase components. we examine the underlying principles behind the various fixed-scale linear interpolating kernels. second, we conceptually extend the properties of the multiple filters to two dimensions to analyze frequency domain characteristics common to all empirically-designed interpolating filters. third, we propose a general linear filter for image interpolation, which uses a universal magnitude response and zero-phase. finally, the proposed filter is further generalized to support arbitrary scaling factors. we claim that at any scaling factor, the proposed algorithm yields low-complexity at a minimal loss of high image-quality with the ability to interpolate diverse image content.
minimax design of adjustable fir filters using 2d polynomial methods. the problem under study here is the minimax design of linear-phase lowpass fir filters having variable passband width and implemented through a farrow structure. we have two main contributions. the first is the design of adjustable fir filters without discretization, using 2d positive trigonometric polynomials, an approach leading to semidefinite programming (sdp) formulation of the design problem. the second is to modify the design problem by a special choice for the passband and stopband edges of the variable fir filter. the advantage is a lower implementation complexity. the new problem is solved using positive hybrid real-trigonometric polynomials and their sdp parameterization. design examples prove the viability of our methods.