waashk/dblp huggingface dataset:
an auditory-based feature for robust speech recognition. a conventional automatic speech recognizer does not perform well in the presence of noise, while human listeners are able to segregate and recognize speech in noisy conditions. we study a novel feature based on an auditory periphery model for robust speech recognition. specifically, gammatone frequency cepstral coefficients are derived by applying a cepstral analysis on gammatone filterbank responses. our evaluations show that the proposed feature performs considerably better than conventional acoustic features. we further demonstrate that integrating the proposed feature with a computational auditory scene analysis system yields promising recognition performance.
a parametric copula based framework for multimodal signal processing. we present a framework for the joint processing of multimodal data such as audio-video data streams. we first consider the problem of estimating the joint distribution of statistically dependent multimodal random variables. we discuss the issues involved and provide a copula based solution. application of this approach to solve a multisensor fusion problem for the detection of a random event is also discussed.
distributed detection of a nuclear radioactive source based on a hierarchical source model. detection of a nuclear radioactive source is considered using a parallel sensor network architecture and a fusion center. a poisson-gamma hierarchical model is used to represent the distribution of the count data received by the sensors. local sensors are assumed to be single threshold binary quantizers that send a vector of sensor decisions over time to the fusion center for global decision-making. using the developed count model, a generalized likelihood ratio test (glrt) using a restricted range mle (rmle) is proposed to declare the global decision. the performance improvement resulting from using the restricted range mle over the unrestricted mle while implementing the glrt is depicted using simulated as well as real data collected from a test-bed using radiation sensors. using bootstrap, 95% confidence bounds on the roc curves, evaluated using real data, are obtained.
regular simplex criterion: a novel feature extraction criterion. feature extraction is an important topic in machine learning. there are two representative criterions for feature extraction, i.e. fisher criterion and maximum margin criterion. in this paper, we propose a new criterion, called regular simplex criterion. this criterion requires that samples from the same class are projected to the same point, while samples from different classes have unit distance. under this criterion, we present a novel dimensionality reduction method, namely linear simplex analysis (lsa). lsa is solved by multivariate linear regression with a specific definition of class indicator matrix which has a strong geometrical interpretation, i.e. each column of this matrix corresponds to a vertex of a regular simplex. several variants of lsa, e.g. regularized simplex analysis (rsa) and kernel simplex analysis (ksa), are also proposed. encouraging experimental results on uci machine learning database indicate that the new criterion as well as the proposed methods are very effective.
correction of b0 inhomogeneity distortion in magnetic resonance spectroscopic imaging. in this paper we reconstruct the 3-d mr spectroscopic data acquired using an echo-planar spectroscopic imaging (epsi) sequence. we propose to compensate for b0 inhomogeneity artifact by using the b0 field map and t2* decay estimated from a higher resolution, multi-slice proton imaging data scanned with similar shimming to the epsi one. we employ total variation (tv) regularization to obtain more uniform spectral peaks and lineshapes, and exploit the sparsity of the spectral data by using the ℓ1 norm in the reconstruction.
a fully affine invariant image comparison method. a fully affine invariant image comparison method, affine-sift (asift) is introduced. while sift is fully invariant with respect to only four parameters namely zoom, rotation and translation, the new method treats the two left over parameters : the angles defining the camera axis orientation. against any prognosis, simulating all views depending on these two parameters is feasible. the method permits to reliably identify features that have undergone very large affine distortions measured by a new parameter, the transition tilt. state-of-the-art methods hardly exceed transition tilts of 2 (sift), 2.5 (harris-affine and hessian-affine) and 10 (mser). asift can handle transition tilts up 36 and higher (see fig. 1).
bit-depth scalable video coding using inter-layer prediction from high bit-depth layer. scalable video coding (svc) is currently developed as an extension of h.264/avc video coding standard. in this paper, we propose three h.264/avc compliant bit-depth scalable video coding schemes, named lh mode (low bit-depth to high bit-depth), hl mode (high bit-depth to low bit-depth) and combined lh-hl mode for different applications. all these schemes efficiently exploit the high correlation between the high bit-depth layer and the low bit-depth layer on macroblock level. experimental results indicate that the hl mode outperforms the other two schemes and it achieves up to 7 db improvement over the simulcast where the high bit-depth video and low bit-depth representations are 12-bit and 8-bit, respectively.
using dependencies to pair samples for multi-view learning. several data analysis tools such as (kernel) canonical correlation analysis and various multi-view learning methods require paired observations in two data sets. we study the problem of inferring such pairing for data sets with no known one-to-one pairing. the pairing is found by an iterative algorithm that alternates between searching for feature representations that reveal statistical dependencies between the data sets, and finding the best pairs for the samples. the method is applied on pairing probe sets of two different microarray platforms.
underwater image enhancement by attenuation inversionwith quaternions. in this paper, an underwater image enhancement method using quaternions is presented. this work aims to improve color rendition and contrast of the objects, as if the scene has been taken out of water. this method is based on light attenuation inversion after processing a color space contraction using quaternions. applied to the white, the attenuation gives a hue vector caracterizing the water color. using this reference axis, geometrical transformations into the color space are computed with quaternions. pixels of water areas of processed images are moved to gray or colors with a low saturation whereas the objects remain fully colored. thus, the contrast of the observed scene is significantly improved and the difference between the background and the rest of the image is increased, giving a first approach towards a pre-segmentation step.
distributions of 3d dct coefficients for video. the three-dimensional discrete cosine transform (3d dct) has been proposed as an alternative to motion-compensated transform coding for video content. however, so far no definitive study has been done on the distribution of 3d dct coefficients of video sequences. this study performs two goodness-of-fit tests, the kolmogorov-smirnov (ks) test and the x2-test, to determine the distribution that best fits the 3d dct coefficients of the luminance components of video sequences with low motion or structured motion. the results indicate that the dc coefficient can be well approximated by a gaussian distribution and a majority of the high-energy ac coefficients can be approximated by a gamma distribution. knowledge of the coefficient distributions can be used to design quantizers optimized for 3d dct coefficients and hence achieve better coding efficiency.
gtd-based transceivers for decision feedback and bit loading. we consider new optimization problems for transceivers with dfe receivers and linear precoders, which also use bit loading at the transmitter. first, we consider the mimo qos (quality of service) problem, which is to minimize the total transmitted power when the bit rate and probability of error of each data stream are specified. the developments of this paper are based on the generalized triangular decomposition (gtd) recently introduced by jiang, li, and hager. it is shown that under some multiplicative majorization conditions there exists a custom gtd-based transceiver which achieves the minimal power. the problem of maximizing the bit rate subject to the total power constraint and given error probability is also considered in this paper. it is shown that the gtd-based systems also give the optimal solutions to the bit rate maximization problem.
a filter bank approach for led illumination sensing based on frequency division multiplexing. in this work, we consider illumination sensing in a light emitting diode (led) based illumination system that normally consists of a large number of leds. illumination sensing is used in order to facilitate the control of such system whose complexity, due to the large number of leds, can be quite high. in this paper, key requirements, i.e. accuracy and speed, on illumination sensing are described. furthermore, we present a filter bank sensor structure based on frequency division multiplexing. the design of the filter response is discussed in the context of supporting maximum number of leds, while the key requirements on illumination sensing are satisfied. in particular, it is shown that, through the use of the filter with a triangular filter response, a large number of leds can be supported in the presence of frequency offsets in a practical range.
the applicability of biased estimation in model and model order selection. biased estimation has the advantage of reducing the mean squared error (mse) of an estimator. the question of interest is how biased estimation affects model selection. in this paper, we introduce biased estimation to a range of model selection criteria. specifically, we analyze the performance of the minimum description length (mdl) criterion based on biased and unbiased estimation and compare it against modern model selection criteria such as kay's conditional model order estimator (cme), the bootstrap and the more recently proposed hook-and-loop resampling based model selection. the advantages and limitations of the considered techniques are discussed. the results indicate that, in some cases, biased estimators can slightly improve the selection of the correct model. we also give an example for which the cme with an unbiased estimator fails, but could regain its power when a biased estimator is used.
perception-based objective speech quality assessment. a joint spectro-temporal auditory model is utilized to assess speech quality objectively. the model mimics early and central auditory functions and serves as a spectro-temporal modulation filterbank. three perceptual relevant parameters, intelligibility, clarity and naturalness, are addressed by the model and are combined to estimate the subjective mean opinion score (mos) for speech quality measure. through a simple multiple linear regression analysis, we demonstrate the performance of our proposed perception-based objective speech quality measure is better than that of the state-of-the-art p.563 standard in estimating mos of the codec-distorted speech in itu-t supp. 23 database.
semi-definite programming approach to sensor network node localization with anchor position uncertainty. the problem of node localization in a wireless sensor network (wsn) with the use of the incomplete and noisy distance measurements between nodes as well as anchor position information is currently an an important yet challenging research topic. most wsn localization studies at present have assumed that the anchor positions are perfectly known which is not valid in the underwater and underground scenarios. in this paper, semi-definite programming (sdp) algorithms are devised for finding the localizations of unknown-position nodes in the presence of anchor position uncertainty. computer simulations are included to contrast the performance of the proposed algorithms with the conventional sdp method and cramér-rao lower bound.
color image feature extraction using color index local auto-correlations. in this paper, we propose a method for extracting color image features, called color index local auto-correlations. pixel color is quantized and described sparsely in a manner similar to the color indexing of color histograms. in addition, by utilizing spatial auto-correlations of the color indexes, the characteristics of color texture can be extracted more effectively than ordinary histogram-based methods. the proposed method has variants in terms of the color space and basic colors used for indexing colors. these various settings are comprehensively compared in the experiments of image retrieval and image classification, and are shown to exhibit favorable results compared to the other conventional methods.
h.264 fast intra mode selection algorithm based on direction difference measure in the pixel domain. in this paper, a fast mode decision algorithm for intra prediction in the h.264/avc is proposed. we use the characteristics of each directional prediction mode to compute the strength of directional differences in the original pixel domain to find the minimal direction error. this is the first time reported in the literature that the intrinsic differences between the real-data and the predictors of modes are used to form an algorithm for mode decision. the approach allows us to select several better candidate modes for evaluation instead of using the full search. experimental results show that the proposed method can achieve more than 80% reduction in computation with negligible degradation in rate-distortion performance, and the results are better than other algorithms available in the literature.
architecture design and implementation of the increasing radius - list sphere detector algorithm. a list sphere detector (lsd) is an enhancement of a sphere detector (sd) that can be used to approximate the optimal map detector. in this paper, we introduce a novel architecture for the increasing radius (ir)-lsd algorithm, which is based on the dijkstra's algorithm. the parallelism possibilities are introduced in the presented architecture, which is also scalable for different multiple-input multiple-output (mimo) systems. the novel architecture is implemented on a virtex-iv field programmable gate array (fpga) chip using high-level ansi c++ language based catapult c synthesis tool from mentor graphics. the used word lengths, the latency of the design, and the required resources are presented and analyzed for 4 × 4 mimo system with 16- quadrature amplitude modulation (qam). the detector implementation achieves a maximum throughput of 12.1mbps at high signal-to-noise ratio (snr).
a watermarking-based method for single-channel audio source separation. in this paper, we address the issue of audio source separation with a single channel, i.e. the estimation of source signals from a single mixture of these signals. this problem is addressed with a specific configuration: source signals are assumed to be available before the mix is processed. we propose an original method that uses a watermarking technique to embed information about the source signals into the mix signal. extracting this watermark enables an end-user who has no access to the original sources to separate these signals from their mixture. thereby several instruments or voice signals can be segregated from a single piece of music to enable post-mixing processing such as volume control.
feature transformation based on discriminant analysis preserving local structure for speech recognition. to improve speech recognition performance, a feature transformation based on discriminant analysis has been widely used to reduce redundant dimensions of features. linear discriminant analysis (lda) and heteroscedastic discriminant analysis (hda) are often used for this purpose, and a generalization method for lda and hda called power lda (plda) has been proposed. however, these methods may result in unexpected dimensionality reduction for multimodal data. it is important to preserve the local structure of the data in reducing the dimensionality of multimodal data. in this paper we introduce two methods, locality preserving hda and locality preserving plda. we also give an efficient calculation scheme to obtain an optimal projection.
lower bounds on the mean square error derived from mixture of linear and non-linear transformations of the unbiasness definition. it is well known that in non-linear estimation problems the ml estimator exhibits a threshold effect, i.e. a rapid deterioration of estimation accuracy below a certain snr or number of snapshots. this effect is caused by outliers and is not captured by standard tools such as the cramér-rao bound (crb). the search of the snr threshold value can be achieved with the help of approximations of the barankin bound (bb) proposed by many authors. these approximations result from a linear transformation (discrete or integral) of the uniform unbiasness constraint introduced by barankin. nevertheless, non-linear transformations can be used as well for some class of p.d.f. including the gaussian case. the benefit is their combination with existing linear transformation to get tighter lower bounds improving the snr threshold prediction.
supervised nonlinear dimensionality reduction by neighbor retrieval. many recent works have combined two machine learning topics, learning of supervised distance metrics and manifold embedding methods, into supervised nonlinear dimensionality reduction methods. we show that a combination of an early metric learning method and a recent unsupervised dimensionality reduction method empirically outperforms previous methods. in our method, the riemannian distance metric measures local change of class distributions, and the dimensionality reduction method makes a rigorous tradeoff between precision and recall in retrieving similar data points based on the reduced-dimensional display. the resulting supervised visualizations are good for finding (sets of) similar data samples that have similar class distributions.
model-based human gait recognition using fusion of features. in this paper, we investigate the fusion of several features extracted from manually-labelled silhouettes. a novel approach for human gait recognition based on the combination of three discriminative features, i.e., the area, the gravity centre, and the orientation of each body component, is also proposed. experimental results show that the proposed method exhibits considerably better performance, in comparison to all existing methods that use manually-extracted silhouettes.
inflating compressed samples: a joint source-channel coding approach for noise-resistant compressed sensing. recently, a lot of research has been done on compressed sensing, capturing compressible signals using random linear projections to a space of radically lower dimension than the ambient dimension of the signal. the main impetus of this is that the radically dimension-lowering linear projection step can be done totally in analog hardware, in some cases even in constant time, to avoid the bottleneck in sensing and quantization steps where a large number of samples need to be sensed and quantized in short order, mandating the use of a large number of fast expensive sensors and a/d converters. reconstruction algorithms from these projections have been found that come within distortion levels comparable to the state of the art in lossy compression algorithms. this paper considers a variation on compressed sensing that makes it resistant to spiky noise. this is achieved by an analog real-field error-correction coding step. it results in a small asymptotic overhead in the number of samples, but makes exact reconstruction under spiky measurement noise, one type of which is the salt and pepper noise in imaging devices, possible. simulations are performed that corroborate our claim and in fact substantially improve reconstruction under unreliable sensing characteristics and are stable even under small perturbations with gaussian noise.
large scale natural image classification by sparsity exploration. we consider in this paper the problem of large scale natural image classification. as the explosion and popularity of images in the internet, there are increasing attentions to utilize millions of or even billions of these images for helping image related research. beyond the opportunities brought by unlimited data, a great challenge is how to design more effective classification methods under these large scale scenarios. most of existing attempts are based on k-nearest-neighbor method. however, in spite of the optimistic performance in some tasks, this strategy still suffers from that, one single fixed global parameter k is not robust for different object classes from different semantic levels. in this paper, we propose an alternative method, called ℓ1-nearest-neighbor, based on a sparse representation computed by ℓ1-minimization. we first treat a testing sample as a sparse linear combination of all training samples, and then consider the related samples as the nearest neighbors of the testing sample. finally, we classify the testing sample based on the majority of these neighbors' classes. we conduct extensive experiments on a 1.6 million natural image database on different semantic levels defined based on wordnet, which demonstrate that the proposed ℓ1-nearest-neighbor algorithm outperforms k-nearest-neighbor in two aspects: 1) the robustness of parameter selection for different semantic levels, and 2) the discriminative capability for large scale image classification task.
hashing the mar coefficients from eeg data for person authentication. electroencephalogram (eeg) recordings of brain waves have been shown to have unique pattern for each individual and thus have potential for biometric applications. in this paper, we propose an eeg feature extraction and hashing approach for person authentication. multi-variate autoregressive (mar) coefficients are extracted as features from multiple eeg channels and then hashed by using our recently proposed fast johnson-lindenstrauss transform (fjlt)-based hashing algorithm to obtain compact hash vectors. based on the eeg hash vectors, a naive bayes probabilistic model is employed for person authentication. our eeg hashing approach presents a fundamental departure from existing methods in eeg-biometry study. the promising results suggest that hashing may open new research directions and applications in the emerging eeg-based biometry area.
a probabilistic approach to clock synchronization of cascaded network elements. precision time protocol (ptp) synchronizes clocks of networked elements by exchanging messages containing precise time-stamps. based on the available timing information, different algorithms can be developed for the clock synchronization. this paper introduces a novel ptp-based method in which clock synchronization is formulated as a probabilistic inference problem and is solved by kalman filtering. the performance of this approach is verified by numerical results.