waashk/dblp huggingface dataset:
shape adaptive estimation of variance in steerable pyramid domain and its application for spatially adaptive image enhancement. in the recent years, denoising based on the spatially adaptive algorithms that employ anisotropic adaption have been developed. these methods are able to match to the local statistics, preserve the edges and truly remove the noise from the texture of the images. on the other hand, a huge proportion of image enhancement methods are implemented in the sparse domains (e.g., wavelets, curvelets, contourlets and steerable pyramid decomposition) due to impressive properties of these transforms such as heavy-tailed nature of marginal distribution, locality and multiresolution. in this paper we try to establish a relation between two mentioned approaches by estimating the local variances of steerable pyramid coefficients using a shape-adaptive window. 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
user behavior modeling in peer-to-peer file sharing networks: dissecting download and removal actions. user behavior models are important for building realistic simulation environment for research on p2p multimedia file-sharing systems. in this paper, we build a user download behavior model and a user removal behavior model, which can describe important user characteristics that are not captured in the existing models. the proposed download behavior model incorporates retry behavior, and the removal behavior model integrates free-riding, file usage, and file removal. based on two-year real user logs, we derive the range of all the model parameters and generate many interesting observations. to validate the proposed models, we compare several models in a case study on the number of living replicas in p2p file-sharing systems. the results demonstrate the accuracy, usability, and advantage of the proposed models.
a multiplicative weight perturbation scheme for distributed beamforming in wireless relay networks with 1-bit feedback. this paper focuses on perturbation-based distributed beamforming with 1-bit feedback for wireless amplify-and-forward relay networks. we propose to use multiplicative perturbations based on givens rotations to adapt the beamforming weights while guaranteeing a sum power constraint for the relays. this perturbation scheme is shown to be computationally efficient and easy to design, thus allowing for low-complexity relay nodes. an adaptation of the givens rotation angle allows to approach optimum performance arbitrarily close. numerical simulations demonstrate noticeable performance gains over additive perturbation schemes that have been exclusively considered up to now.
coping with out-of-vocabulary words: open versus huge vocabulary asr. this paper investigates methods for coping with out-of-vocabulary words in a large vocabulary speech recognition task, namely the automatic transcription of italian broadcast news. two alternative ways for augmenting a 64k(thousand)-word recognition vocabulary and language model are compared: introducing extra words with their phonetic transcription up to 1.2m (million) words, or extending the language model with so-called graphones, i.e. subword units made of phone-character sequences. graphones and phonetic transcriptions of words are automatically generated by adapting an off-the-shelf statistical machine translation toolkit. we found that the word-based and graphone-based extensions allow both for better recognition performance, with the former performing significantly better than the latter. in addition, the word-based extension approach shows interesting potential even under conditions of little supervision. in fact, by training the grapheme to phoneme translation system with only 2k manually verified transcriptions, the final word error rate increases by just 3% relative, with respect to starting from a lexicon of 64k words.
fourier-based modeling of topologically complex bone data using various alternatives of 3d scalar fields. this article presents a new approach for fourier-based modeling of bone anatomies for compression and smoothing. by treating the bone surface as a level set of a 31) scalar field, we can model topologically complex models such as bones. in particular, we experiment with five different alternatives, and prove that 31) scalar field which allows monotonous continuity around the boundary can be a good choice for volumetric description of the surface. this allows avoiding gibbs phenomena which previous volume-based methods have suffered from, returning better results in compression and smoothing than other scalar fields. we demonstrate the efficacy of the proposed method by showing results with various bone data.
stochastic resource allocation for orthogonal access based on quantized csi: optimality, convergence and delay analysis. dynamic allocation of power, rate and channel access is a critical task in wireless networks. capitalizing on convex optimization and stochastic approximation tools, this paper develops a stochastic resource allocation algorithm that minimizes average transmit power under individual average rate constraints. focus is placed on networks where users transmit orthogonally over a set of parallel channels and transmissions are adapted based on quantized channel state information (csi) allowing even channel statistics to be unknown. convergence of the developed stochastic scheme is characterized and the average queue delays are obtained in closed form.
candidate proposal for itu-t super-wideband speech and audio coding. this paper describes the speech and audio codec that has been submitted to itu-t by huawei and etri as a candidate for the upcoming super-wideband and stereo extensions of rec. g.729.1 and g.718. the core codec in the current implementation is g.729.1 and the encoded frequency range is increased from 7 khz to 14 khz. therefore, the maximum bit rate is raised from 32 kbit/s to 64 kbit/s by adding five bitstream layers. a comprehensive overview of the codec is presented with a focus on the mono coding components. the results of the listening tests that have been conducted during the itu-t qualification phase are summarized. the proposed codec passes all quality requirements for mono input signals.
second-order differential adaptive microphone array. an adaptive second-order differential microphone design is proposed here that is constructed from a weighted sum of omnidirectional microphones. theoretically, only three microphones are required to form a second-order array. the three microphone signals are combined to form three unique fixed second-order beams. any second-order differential beampattern can be realized using a weighted sum of these three “building-block” beam outputs. if certain simple constraints are placed on the weighting of the three fixed beams, the two null locations that define the final second-order beampattern can be constrained to defined angular regions.
square-root free orthogonalization algorithms. this paper is concerned with the derivation and analysis of higher order algorithms of polynomial type for computing an orthonormal basis of a subspace. these algorithms are derived from unconstrained optimization of certain cost functions. the proposed methods are efficient and do not require square root computation. based on these, algorithms for orthonormalization with respect to a positive definite matrix and principal and minor subspace methods are developed. numerical experiments illustrate the theoretical results.
distributed optimization in an energy-constrained network using a digital communication scheme. we consider a distributed optimization problem where n nodes, sl, l ∈ {1, …, n}, wish to minimize a common strongly convex function ƒ(x), x = [x1, … , xn]t , and suppose that node sl only has control of variable xl. the nodes locally update their respective variables and periodically exchange their values over noisy channels. previous studies of this problem have mainly focused on the convergence issue and the analysis of convergence rate. in this work, we focus on the communication energy and study its impact on convergence. in particular, we study the minimum amount of communication energy required for nodes to obtain an ∈-minimizer of ƒ(x) in the mean square sense. in an earlier work, we considered analog communication schemes and proved that the communication energy must grow at the rate of ω(∈−1) to obtain an ∈-minimizer of a convex quadratic function. in this paper, we consider digital communication schemes and propose a distributed algorithm which only requires communication energy of o ((log ∈−1)3) to obtain an ∈-minimizer of ƒ(x). furthermore, the algorithm provided herein converges linearly. thus, distributed optimization with digital communication schemes is significantly more energy efficient than with analog communication schemes.
fast noise psd estimation with low complexity. although noise psd estimation is a crucial part of noise reduction algorithms, most noise psd estimators have problems in tracking non-stationary noise sources. recently, a noise psd estimator based on dft-subspace decompositions was proposed, which improves estimation of the psd of such noise sources. however, as this approach is based on eigenvalue decompositions per dft bin, it might be too computationally demanding for low-complexity applications like hearing aids. in this paper we present a method with similar noise tracking performance as the dft-subspace approach, but with low computational costs. this method is based on computation of high resolution perodiograms, and can estimate the noise psd when both speech and noise are present in a frequency bin. when combined with a complete noise reduction system, the proposed method can lead to an improvement for non-stationary noise sources of more than 1 db segmental snr and 0.3 on a pesq scale, compared to standard noise tracking methods such as minimum statistics and the quantile based approach, while computational complexity is in the same order of magnitude.
a speech presence microphone array beamformer using model based speech presence probability estimation. the purpose of this study is to investigate the performance of speech presence (sp) microphone array beamforming. when the presence uncertainty of the desired speech is considered, noise reduction is greatly achieved while preserving low speech distortion level. furthermore, we propose a novel model based speech presence probability (spp) estimator, exploring both the sinusoid structure of speech and signal-to-noise ratio (snr). finally, experiments verify the effectiveness of the proposed sp-beamformer, resulting in a better trade-off between speech distortion and noise leakage, and a corresponding higher output segmental snr, when compared with the classical beamformers.
effective metric-based speaker segmentation in the frequency domain. in this paper, we present an approach, called freqdist, for speaker segmentation based on a distance measurement applied in the frequency domain. to enhance the detection performance, the spectrum is reweighted using normalization techniques. additionally, noise-like (i.e. flat) spectra are removed based on the entropy. experiments using the timit database [1] and westdeutscher rundfunk broadcast data show that our segmentation approach yields a good performance compared to the distbic algorithm [2]. in particular, for the timit data our algorithm reaches a false alarm rate (far) less than half of the value of the distbic algorithm and a missed detection rate (mdr) of 7.0% instead of 13.1%.
an error robust ultra low delay audio coder using an ma prediction model. this paper compares two prediction structures for predictive perceptual audio coding in the context of the ultra low delay (uld) coding scheme. one structure is based on the commonly used ar signal model, leading to an iir predictor in the decoder. the other structure is based on an ma signal model, leading to an fir predictor in the decoder. we find that the ar-based predictor has a slightly better performance in case of an undisturbed transmission channel, but the ma-based predictor has a much better performance in case of transmission errors. for a bit error rate (ber) of 1.0e-5, the perceptual quality of the proposed ma model predictor achieves a mean objective difference grade (odg) of −0.66 odg whereas the ar model predictor only reaches −3.42 odg.
compressive wide-band spectrum sensing. we present a compressive wide-band spectrum sensing scheme for cognitive radios. the received analog signal at the cognitive radio sensing receiver is transformed in to a digital signal using an analog-to-information converter. the autocorrelation of this compressed signal is then used to reconstruct an estimate of the signal spectrum. we evaluate the performance of this scheme in terms of the mean squared error of the power spectrum density estimate and the probability of detecting signal occupancy.
power level difference as a criterion for speech enhancement. this paper deals with the problem of speech enhancement in near field condition, when two microphones are available. proposed technique relies on the difference in power of received signals at the two microphones. this difference is employed to estimate the clean speech signal power. the method has the capability of dealing with non-stationary noise, a drawback of many noise reduction techniques. superiority of the presented method over some of prominent methods in this field is demonstrated by conducting both subjective and objective quality tests.
narrowband interference mitigation in bicm ofdm systems. orthogonal frequency division multiplexing (ofdm) is noted for its resistance to narrowband interference when equipped with forward error correction. this technique along with erasure insertion is adequate when the the signal-to-interference ratio (sir) is moderate, around 0 db, however, when the interference is severe, i.e. sir = −20 db, a non-orthogonal interference corrupts a large number of the data subcarriers due to spectral leakage. this situation requires a large number of erasures that compromise the code's error correction capability. the prediction-error filter (pef) is proposed as an erasure insertion mechanism that localizes the erasures to the tones surrounding the interference, while not affecting the remaining tones. the simulation results indicate excellent performance, for the bit-interleaved coded modulated (bicm) ofdm system using the pef and bpsk modulation when the sir = −20 db in a frequency-selective fading channel.
compression of line spectral frequency parameters with asynchronous interpolation. tts systems require a trade-off between size and speech quality. a larger acoustic inventory allows synthesis of speech that sounds more natural. the asynchronous interpolation model improves the quality to size ratio, allowing better compression of large acoustic inventories, as well as better quality speech from a small system. at maximum compression, our method represents most phonemes by a single frame of data. coarticulation effects are specified as context-specific non-linear interpolation functions. dividing the speech features into multiple data streams allows asynchronous interpolation. in this study, aim was applied to lsf parameters. varying the number of streams allows for variable amount of compression. we used three different objective measures to investigate the effect of number and partitioning of streams. the first few weight functions (and the last one) seem to offer the most error reduction. partitions separating the first 6 lsfs score well with all three measures.
product-hmms for automatic sign language recognition. we address multistream sign language recognition and focus on efficient multistream integration schemes. alternative approaches are investigated and the application of product-hmms (phmm) is proposed. the phmm is a variant of the general multistream hmm that also allows for partial asynchrony between the streams. experiments in classification and isolated sign recognition for the greek sign language using different fusion methods, show that the phmms perform the best. fusing movement and shape information with the phmms has increased sign classification performance by 1,2% in comparison to the parallel hmm fusion model. isolated sign recognition rate increased by 8,3% over movement only models and by 1,5% over movement-shape models using multistream hmms.
the speed of greed: characterizing myopic gossip through network voracity. this paper analyzes the rate of convergence of greedy gossip with eavesdropping (gge). in previous work, we proposed gge, a fast gossip algorithm based on exploiting the broadcast nature of wireless communications rather than location information. assuming all transmissions are wireless broadcasts, nodes can keep track of their neighbors' values by eavesdropping on their communications. then, when it comes time to gossip, a node greedily and myopically gossips with the neighbor whose value is most different from its own, rather than with a randomly chosen neighbor. previously, we have proved that gge converges to the average consensus on connected network topologies and demonstrated that gge outperforms standard randomized gossip (rg). in this paper we study the rate of convergence of gge in terms of network voracity which is a topology-dependent constant analogous to the second-largest eigenvalue characterization for rg. simulations demonstrate that the convergence rate of gge is superior to existing average consensus algorithms such as geographic gossip.
real-time implementation of robust face detection on mobile platforms. although many face detection algorithms have been introduced in the literature, only a handful of them can meet the real-time constraints of mobile devices. this paper presents the real-time implementation of our previously introduced face detection algorithm on a mobile device. the steps taken to achieve such a real-time implementation are discussed. real-time comparison results with the widely used viola-jones face detection algorithm in terms of detection rate and processing speed are presented to demonstrate the robustness of our real-time solution.
a gmm supervector kernel with the bhattacharyya distance for svm based speaker recognition. gaussian mixture model (gmm) supervector is one of the effective techniques in text independent speaker recognition. in our previous work, we introduce the gmm-ubm mean interval (gumi) concept based on the bhattacharyya distance. subsequently gumi kernel was successfully used in conjunction with support vector machine (svm) for speaker recognition. besides the first order statistics, it is generally believed that speaker cues are also partly conveyed by second order statistics. in this paper, we extend the bhattacharyya-based svm kernel by constructing the supervector with the mean statistical vector and the covariance statistical vector. comparing with the kullback-leibler (kl) kernel, we demonstrate the effectiveness of the new kernel on the 2006 national institute of standards and technology (nist) speaker recognition evaluation (sre) dataset.
exploring functional connectivity in fmri via clustering. in this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fmri. in particular, we consider the k-means and spectral clustering algorithms as alternatives to the commonly used seed-based analysis. to enable clustering of the entire brain volume, we use the nyström method to approximate the necessary spectral decompositions. we apply k-means, spectral clustering and seed-based analysis to resting-state fmri data collected from 45 healthy young adults. without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via seed-based analysis. our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.
dna coding using finite-context models and arithmetic coding. the interest in dna coding has been growing with the availability of extensive genomic databases. although only two bits are sufficient to encode the four dna bases, efficient lossless compression methods are still needed due to the size of dna sequences and because standard compression algorithms do not perform well on dna sequences. as a result, several specific coding methods have been proposed. most of these methods are based on searching procedures for finding exact or approximate repeats. low order finite-context models have only been used as secondary, fall back mechanisms. in this paper, we show that finite-context models can also be used as main dna encoding methods. we propose a coding method based on two finite-context models that compete for the encoding of data, on a block by block basis. the experimental results confirm the effectiveness of the proposed method.
unsupervised acoustic and language model training with small amounts of labelled data. we measure the effects of a weak language model, estimated from as little as 100k words of text, on unsupervised acoustic model training and then explore the best method of using word confidences to estimate n-gram counts for unsupervised language model training. even with 100k words of text and 10 hours of training data, unsupervised acoustic modeling is robust, with 50% of the gain recovered when compared to supervised training. for language model training, multiplying the word confidences together to get a weighted count produces the best reduction in wer by 2% over the baseline language model and 0.5% absolute over using unweighted transcripts. oracle experiments show that a larger gain is possible, but better confidence estimation techniques are needed to identify correct n-grams.
automatic prosodic events detection using syllable-based acoustic and syntactic features. automatic prosodic event detection is important for both speech understanding and natural speech synthesis since prosody provides additional information over the short-term segmental features and lexical representation of an utterance. similar to previous work, this paper focuses on automatic detection of coarse level representation of pitch accents, intonational phrase boundaries (ipb), and break indices. we exploit various classifiers and identify effective feature sets to improve performance of prosodic event detection according to acoustic, lexical, and syntactic evidence. our experiments on the boston university radio news corpus show that the neural network classifier achieves the best performance for modeling acoustic evidence, and that support vector machines are more effective for the lexical and syntactic evidence. the combination of the acoustic and the syntactic models yields 89.8% accent detection accuracy, 93.3% ipb detection accuracy, and 91.1% break index detection accuracy. compared with previous work, the ipb perfromance is similar, whereas the results for accent and break index detection are significantly better.
subspace based doa estimation in the presence of correlated signals and model errors. high-resolution subspace based direction-of-arrival (doa) estimation requires a number of assumptions about the signal and antenna. in our application automotive radar, among many practical problems, signals are often correlated and the antenna is not calibrated. this difficult combination has been seldom addressed in the literature. in this paper, we study their simultaneous impact on doa estimation, describe a so called “coherent model error interference” phenomenon, propose a prewhitening scheme for algebraic subspace based doa estimation, and show some simulation results.