waashk/dblp huggingface dataset:
an algorithm for speech segregation of co-channel speech. this paper introduces an algorithm to separate speech streams from a single-channel speech mixture. most current speech segregation algorithms allocate speech regions to participating speakers depending on which speaker dominates in which spectro-temporal region. the proposed method is a different approach to speech segregation, in that it separates the participating speaker streams rather than decide in the favor of the dominating speaker. the algorithm depends on a lease-squares fitting approach to model the speech mixture as a sum of complex exponentials. the algorithm gives results that are better than an existent algorithm when tested on the same task. the performance on a different database yielded good segregation results, even for target-to-masker ratios as low as −15 db. the algorithm has immense promise for improvement and practical implementation.
shadow detection for moving humans using gradient-based background subtraction. cast shadows cause serious problems in the functionality of vision-based applications, such as video surveillance, traffic monitoring and various other applications. accurate detection and removal of cast shadows is a challenging task. common shadow detection techniques normally use color information, which is not a reliable base in every scenario. this paper presents a novel scheme for real time detection of cast shadows using contour like structures of objects, which are obtained by gradient-based background subtraction. the scheme does not use any color information. two basic rules are followed for shadow detection. the first rule is that shadows do not change the texture of the background. the second rule is a cast shadow lies outside the boundary of an object and has a relatively small common boundary with the object. experimental results show the performance of the proposed scheme. objective evaluation shows that the algorithm classifies 90 percent of the pixels of the objects and their shadow correctly.
utterance verification using improved confidence measures based on alignment confusion rate in chinese digits recognition. in this paper, we explore an approach to improved confidence measures based on a novel alignment confusion rate (acr) which integrates alignment information from two different modeling unit sets in chinese digits recognition system. both initial-final (if) phone set and head-body-tail (hbt) models have proven to obtain good recognition performance for connected digit strings. these two different modeling can produce similar results but with different time-marked word boundaries. the objective of our proposed method is combining posterior probability with alignment confusion rate score provided by word alignment of if-based results to hbt-based reference results that minimizes word error rate to get an effective confidence measure for utterance verification. the efficiency of the proposed algorithm is demonstrated with various experiments on data collected from car-kit microphone.
a cost-error optimized architecture for 9/7 lifting based discrete wavelet transform with balanced pipeline stages. discrete wavelet transform (dwt) is increasingly recognized in image/video compression standards, as indicated by its use in jpeg2000. the lifting scheme algorithm is an alternative dwt implementation that has a lower computational complexity. in this paper, a new high performance lifting-based architecture is presented for the 9/7 dwt engine. the proposed architecture has a balanced pipeline and improves both the computational error and hardware complexity for any given working frequency. in the proposed architecture, the constant coefficients are modified by introducing new variables to the conventional lifting structure to minimize hardware cost and computational error, imposed by quantization of coefficients. simulation results indicate a quality improvement of up to 15 db when compared to an architecture using the standard coefficients that has the same hardware cost and working frequency. similarly, the hardware cost is reduced by about 20% when both architectures deliver the same psnr when operating at the same frequency.
a scale transform based method for rhythmic similarity of music. this paper introduces scale transforms to measure rhythmic similarity between two musical pieces. the rhythm of a piece of music is described by the scale transform magnitude, computed by transforming the sample autocorrelation of its onset strength signal to the scale domain. then, two pieces can be compared without the impact of tempo differences by using simple distances between these descriptors like the cosine distance. a widely used dance music dataset has been chosen for proof of concept. on this data set, the proposed method based on scale transform achieves classification results as high as other state of the art approaches. on a second data set, which is characterized by much larger intra-class tempo variance, the scale transform based measure improves classification compared to previously presented measures by 41%.
l1 regularized super-resolution from unregistered omnidirectional images. in this paper, we address the problem of super-resolution from multiple low-resolution omnidirectional images with inexact registration. such a problem is typically encountered in omnidirectional vision scenarios with reduced resolution sensors in imperfect settings. several spherical images with arbitrary rotations in the so(3) rotation group are used for the reconstruction of higher resolution images. we propose an l1 regularized total least squares normminimization method for joint registration and reconstruction with better stabilization and denoising. experimental results show that regularization offers a quality improvement of up to 1db. in addition, it reduces the number of low resolution images that are necessary to reconstruct a high resolution image at a target quality.
maximizing global entropy reduction for active learning in speech recognition. we propose a new active learning algorithm to address the problem of selecting a limited subset of utterances for transcribing from a large amount of unlabeled utterances so that the accuracy of the automatic speech recognition system can be maximized. our algorithm differentiates itself from earlier work in that it uses a criterion that maximizes the lattice entropy reduction over the whole dataset. we introduce our criterion, show how it can be simplified and approximated, and describe the detailed algorithm to optimize the criterion. we demonstrate the effectiveness of our new algorithm with directory assistance data collected under the real usage scenarios and show that our new algorithm consistently outperforms the confidence based approach by a significant margin. using the algorithm cuts the number of utterances needed for transcribing by 50% to achieve the same recognition accuracy obtained using the confidence-based approach, and by 60% compared to the random sampling approach.
speech rhythm guided syllable nuclei detection. in this paper, we present a novel speech-rhythm-guided syllable-nuclei location detection algorithm. as a departure from conventional methods, we introduce an instantaneous speech rhythm estimator to predict possible regions where syllable nuclei can appear. within a possible region, a simple slope based peak counting algorithm is used to get the exact location of each syllable nucleus. we verify the correctness of our method by investigating the syllable nuclei interval distribution in timit dataset, and evaluate the performance by comparing with a state-of-the-art syllable nuclei based speech rate detection approach.
fast algorithm for gmm-based pattern classifier. this work proposes a fast decision algorithm in pattern classification based on gaussian mixture models (gmm). statistical pattern classification problems often meet a situation that comparison between probabilities is obvious and involve redundant computations. when gmm is adopted for the probability model, the exponential function should be evaluated. this work firstly reduces the exponential computations to simple and rough interval calculations. the exponential function is realized by scaling and multiplication with powers of two so that the decision is efficiently realized. for finer decision, a refinement process is also proposed. in order to verify the significance, experimental results on ti dm6437 evm board are shown through the application to a skin-color extraction problem. it is verified that the classification was almost completed without any refinement process and the refinement process can proceed the residual decisions.
tracking of random number of targets with random number of sensors using random finite set theory. variation in the number of targets and sensors needs to be addressed in any realistic sensor system. targets may come in or out of a region or may suddenly stop emitting detectable signal. sensors can be subject to failure for many reasons. we derive a tracking algorithm with a model that includes these variations using random finite set theory (rfst). rfst is a generalization of standard probability theory into the finite set theory domain. this generalization does come with additional mathematical complexity. however, many of the manipulations in rsft are similar in behavior and intuition to those of standard probability theory.
high-level feature extraction using svm with walk-based graph kernel. we investigate a method using support vector machines (svms) with walk-based graph kernels for high-level feature extraction from images. in this method, each image is first segmented into a finite set of homogeneous segments and then represented as a segmentation graph where each vertex is a segment and edges connect adjacent segments. given a set of features associated with each segment, we then obtain a positive definite kernel between images by comparing walks in the respective segmentation graphs, and image classification is carried out with an svm based on this kernel. in a benchmark experiment on the mediamill challenge problem, the mean average precision increased from 0.216 (baseline) to 0.341 when our method was utilized.
modeling characters versuswords for mandarin speech recognition. word based models are widely used in speech recognition since they typically perform well. however, the question of whether it is better to use a word-based or a character-based model warrants being for the mandarin chinese language. since chinese is written without any spaces or word delimiters, a word segmentation algorithm is applied in a pre-processing step prior to training a word-based language model. chinese characters carry meaning and speakers are free to combine characters to construct new words. this suggests that character information can also be useful in communication. this paper explores both word-based and character-based models, and their complementarity. although word-based modeling is found to outperform character-based modeling, increasing the vocabulary size from 56k to 160k words did not lead to a gain in performance. results are reported for the gale mandarin speech-to-text task.
joint estimation and compensation of frequency, dc-offset, i-q imbalance and channel in mimo receivers. direct-conversion is a low-cost rf architecture that requires fewer external components in chip implementation. it introduces, however, extra rf impairments such as i-q imbalance and dc-offset that degrade severely the receiver performance if left uncompensated. in this paper, a new method of joint estimation and compensation of frequency, dc-offset, i-q imbalance and channel is developed for mimo receivers. numerical results show that the proposed method has a very good performance; less than 2 db loss is observed at bit-error rate 10−4, as compared to the ideal receiver. in addition, a new training sequence along with a low-complexity architecture is proposed to reduce largely the receiver complexity with negligible performance loss.
delay-based overlay construction in p2p video broadcast. we consider streaming video content over an overlay network of peer nodes. each of the nodes employs a mesh-pull mechanism to organize the download of data units from its neighbours. we propose a novel algorithm for constructing the distribution overlay, where peers are arranged in neighbourhoods that exhibit similar latency values from the origin media server. such an organization increases data sharing between neighbours in broadcast applications and reduces the play-out latency at a peer. each of the nodes in the overlay is further equipped with a packet scheduling procedure that requests data units from neighbours in the order of their importance and their popularity within the neighbourhood. finally, requesting peers share the upload bandwidth of a sending peer in proportion to their transmission rate to that peer in order to discourage free-riding in the system. our simulation results show that the proposed mesh construction procedure provides improved performance in terms of frame-freeze and playback latency relative to a conventional approach where peer neighbours are selected at random. corresponding gains in video quality for the media presentation are also registered due to the improved continuity of the playback experience.
denoising scheme for realistic digital photos from unknown sources. this paper targets denoising of digital photos taken by cameras with unknown sensor parameters and image processing pipeline. common noise characteristics in such images originate from camera-internal processing, such as demosaicing, tone mapping, and jpeg compression. three of the noise characteristics that are not adequately addressed by existing denoising algorithms are spatially correlated low-frequency noise, strong signal dependency of the noise level and high levels of the chrominance noise relative to the luminance noise. we propose a generic scheme that extends existing denoisers such as the bilateral filter to account for all the problems above. our solution combines a novel progressive pyramidal filtering scheme to address the correlated noise, filter adaptation via local noise level estimation and luminance-guided chrominance filtering to address the low-snr of the chrominance channels. we demonstrate the effectiveness of our solution for challenging realistic noisy photos.
a study on recognizing distorted speech over local distributed transducer networks. in a collaborative scenario, a multiplicity of portable devices may constitute a network of distributed microphones, without a clearly defined geometric configuration or synchronization that can be taken advantage of for traditional microphone array processing to enhance the acquired signal. this application scenario represents a severe, but interesting challenge for automatic speech recognition systems. in this paper, we investigate a variety of robust speech recognition techniques with a focus on the distributed transducer scenario. we also report some important study results that lead to new thinking in the design of robust speech recognition for broadened applications. two issues that are inherent to distributed transducer networks are specially investigated. first, we study the effect of the sampling rate skew of microphones to the system performance; second, we explore the possibility of combining recognition hypotheses from multiple transducer channels for improved recognition accuracy.
nonparametric spectral density estimation with missing observations. self-consistency is a fundamental principle in statistics for retaining maximum amount of information in the data. in this paper this principle is applied to develop a new method for nonparametric spectrum estimation with missing data. one major advantage of the proposed method is that it can be coupled with any complete data nonparametric spectrum estimation procedure, including kernel smoothing, wavelet and spline estimators. the practical performance of the method is illustrated by a simulation study.
feature based classification of computer graphics and real images. photorealistic images can now be created using advanced techniques in computer graphics (cg). synthesized elements could easily be mistaken for photographic (real) images. therefore we need to differentiate between cg and real images. in our work, we propose and develop a new framework based on an aggregate of existing features. our framework has a classification accuracy of 90% when tested on the de facto standard columbia dataset, which is 4% better than the best results obtained by other prominent methods in this area. we further show that using feature selection it is possible to reduce the feature dimension of our framework from 557 to 80 without a significant loss in performance (≪ 1%). we also investigate different approaches that attackers can use to fool the classification system, including creation of hybrid images and histogram manipulations. we then propose and develop filters to effectively detect such attacks, thereby limiting the effect of such attacks to our classification system.
blind soft-output equalization of block-oriented wireless communications. we consider blind equalization for block transmissions over the frequency selective rayleigh fading channel. in the absence of pilot symbols, the receiver must be able to perform joint equalization and blind channel identification. relying on a mixed discrete-continuous state-space representation of the communication system, we introduce a blind bayesian equalization algorithm based on a gaussian mixture parameterization of the a posteriori probability density function (pdf) of the transmitted data and the channel. the performances of the proposed algorithm are compared with existing blind equalization techniques using numerical simulations. 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
doubly weighted nonnegative matrix factorization for imbalanced face recognition. we propose in this paper a novel doubly weighted nonnegative matrix factorization (dwnmf) method for imbalanced face recognition. motivated by the fact that some face samples and certain parts of each face sample are more useful for recognition, we construct two weighted matrices based on the pairwise similarity of face samples in the same class and the discriminant score of each face pixel. compared with the existing nmf algorithm, the proposed dwnmf method can more effectively exploit the discriminative and geometrical information of face samples, and it is especially suitable for imbalanced face recognition. experimental results are presented to demonstrate the efficacy of the proposed method.
multi-modal speaker diarization of real-world meetings using compressed-domain video features. speaker diarization is originally defined as the task of determining “who spoke when” given an audio track and no other prior knowledge of any kind. the following article shows a multi-modal approach where we improve a state-of-the-art speaker diarization system by combining standard acoustic features (mfccs) with compressed domain video features. the approach is evaluated on over 4.5 hours of the publicly available ami meetings dataset which contains challenges such as people standing up and walking out of the room. we show a consistent improvement of about 34% relative in speaker error rate (21% der) compared to a state-of-the-art audio-only baseline.
semi-supervised ensemble tracking. in this paper, we propose a semi-supervised ensemble tracking approach under the framework of particle filter. the particle filter is used not only for object searching, but also for unlabelled sample generation. by adopting the semi-supervised learning technology, these unlabelled samples which are generated online are utilized to progressively modify the classifier and make the ensemble tracker to be more robust to environment changing. on the other hand, utilizing semi-supervised learning technology can avoid the drifting phenomenons which are often encountered when using supervised learning. finally, the performance of the proposed approach is evaluated using real visual tracking examples.
the fractional hilbert transform and dual-tree gabor-like wavelet analysis. we provide an amplitude-phase representation of the dual-tree complex wavelet transform by extending the fixed quadrature relationship of the dual-tree wavelets to arbitrary phase-shifts using the fractional hilbert transform (fht). the fht is a generalization of the hilbert transform that extends the quadrature phase-shift action of the latter to arbitrary phase-shifts—a real shift parameter controls this phase-shift action. next, based on the proposed representation and the observation that the fht operator maps well-localized b-spline wavelets (that resemble gaussian-windowed sinusoids) into b-spline wavelets of the same order but different shift, we relate the corresponding dual-tree scheme to the paradigm of multiresolution windowed fourier analysis.
acoustically discriminative training for language models. this paper introduces a discriminative training for language models (lms) by leveraging phoneme similarities estimated from an acoustic model. to train an lm discriminatively, we needed the correct word sequences and the recognized results that automatic speech recognition (asr) produced by processing the utterances of those correct word sequences. but, sufficient utterances are not always available. we propose to generate the probable n-best lists, which the asr may produce, directly from the correct word sequences by leveraging the phoneme similarities. we call this process the “pseudo-asr”. we train the lm discriminatively by comparing the correct word sequences and the corresponding n-best lists from the pseudo-asr. experiments with real-life data from a japanese call center showed that the lm trained with the proposed method improved the accuracy of the asr.
a fast and efficient heuristic nuclear-norm algorithm for affine rank minimization. the problem of affine rank minimization seeks to find the minimum rank matrix that satisfies a set of linear equality constraints. generally, since affine rank minimization is np-hard, a popular heuristic method is to minimize the nuclear norm that is a sum of singular values of the matrix variable [1]. a recent intriguing paper [2] shows that if the linear transform that defines the set of equality constraints is nearly isometrically distributed and the number of constraints is at least o(r(m + n) logmn), where r and m × n are the rank and size of the minimum rank matrix, minimizing the nuclear norm yields exactly the minimum rank matrix solution. unfortunately, it takes a large amount of computational complexity and memory buffering to solve the nuclear norm minimization problem with known nearly isometric transforms. this paper presents a fast and efficient algorithm for nuclear norm minimization that employs structurally random matrices [3] for its linear transform and a projected subgradient method that exploits the unique features of structurally random matrices to substantially speed up the optimization process. theoretically, we show that nuclear norm minimization using structurally random linear constraints guarantees the minimum rank matrix solution if the number of linear constraints is at least o(r(m+n) log3 mn). extensive simulations verify that structurally random transforms still retain optimal performance while their implementation complexity is just a fraction of that of completely random transforms, making them promising candidates for large scale applications.