waashk/dblp huggingface dataset:
a pixel-wise, learning-based approach for occlusion estimation of iris images in polar domain. on normalized iris images, there are many kinds of noises, such as eyelids, eyelashes, shadows or specular reflections, that often occlude the true iris texture. if high recognition rate is desired, those occluded areas must be estimated accurately in order for them to be excluded during the matching stage. in this paper, we propose a unified, probabilistic and learning-based approach to estimate all kinds of occlusions within one unified model. experiments have shown that our method not only estimates occlusion very accurately, but also does it with high speed, which makes it useful for practical iris recognition systems.
classification via group sparsity promoting regularization. recently a new classification assumption was proposed in [1]. it assumed that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. the classification algorithm in [1] was based on the idea that all the correlated training samples belonging to the correct class are used to represent the test sample. the lasso regularization was proposed to select the representative training samples from the entire training set (consisting of all the training samples). lasso however tends to select a single sample from a group of correlated training samples and thus does not promote the representation of the test sample in terms of all the training samples from the correct group. to overcome this problem, we propose two alternate regularization methods, elastic net and sum-over-l2-norm. both these regularization methods favor the selection of multiple correlated training samples to represent the test sample. experimental results on benchmark datasets show that our regularization methods give better recognition results compared to [1].
neural network based language models for highly inflective languages. speech recognition of inflectional and morphologically rich languages like czech is currently quite a challenging task, because simple n-gram techniques are unable to capture important regularities in the data. several possible solutions were proposed, namely class based models, factored models, decision trees and neural networks. this paper describes improvements obtained in recognition of spoken czech lectures using language models based on neural networks. relative reductions in word error rate are more than 15% over baseline obtained with adapted 4-gram backoff language model using modified kneser-ney smoothing.
instantaneous pose estimation using rotation vectors. an algorithm for estimating the pose, i.e., translation and rotation, of an extended target object is introduced. compared to conventional methods, where pose estimation is performed on the basis of time-of-flight (tof) measurements between external sources and sensors attached to the object, the proposed approach directly uses the amplitude values measured at the sensors for estimation purposes without an intermediate tof estimation step. this is achieved by modeling the wave propagation by a nonlinear dynamic system comprising a system and a measurement equation. the nonlinear system equation includes a model of the time-variant structure of the object rotation based on rotation vectors. as a result, the measured amplitude values at the sensors can be processed instantaneously in a recursive fashion. uncertainties in the measurement process are systematically considered by employing a stochastic filter for estimating the pose, i.e., the state of the nonlinear dynamic system.
a low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries. we propose a variant of orthogonal matching pursuit (omp), called locomp, for scalable sparse signal approximation. the algorithm is designed for shift-invariant signal dictionaries with localized atoms, such as time-frequency dictionaries, and achieves approximation performance comparable to omp at a computational cost similar to matching pursuit. numerical experiments with a large audio signal show that, compared to omp and gradient pursuit, the proposed algorithm runs in over 500 less time while leaving the approximation error almost unchanged.
mobile media search. this panel paper presents motivations for discussing mobile media search and contains statements from the panelists who are industry research leaders in this field.
unified speech and audio coding scheme for high quality at low bitrates. traditionally, speech coding and audio coding were separate worlds. based on different technical approaches and different assumptions about the source signal, neither of the two coding schemes could efficiently represent both speech and music at low bitrates. this paper presents a unified speech and audio codec, which efficiently combines techniques from both worlds. this results in a codec that exhibits consistently high quality for speech, music and mixed audio content. the paper gives an overview of the codec architecture and presents results of formal listening tests comparing this new codec with he-aac(v2) and amr-wb+. this new codec forms the basis of the reference model in the ongoing mpeg standardization activity for unified speech and audio coding.
signal processing challenges for future wireless communications. the mobile internet has finally arrived with the sky-rocketing usage increase of hspa. lte, wimax and evolved wifi are the upcoming wireless standards which are based on ofdm and mimo. the mentioned standards have already started the evolution process with lte-advanced, ieee 802.16m and ieee 802.11n. new technology building blocks are on its way like cooperative mimo, relaying and self-organizing networks. increase of data-rate is one target, but the energy consumption both on the terminal and network side are important as well. this paper discusses the challenges imposed by developments in the wireless industry. advanced signal processing can play a major role in addressing these challenges.
language identification of individualwords in a multilingual automatic speech recognition system. this paper presents a new algorithm for identifying the language of words in a multilingual automatic speech recognition system. the new algorithm uses as input written words and it is composed of a method for language modelling and a method to select the language of a given word based on the available models. we also present two selection rules for the model's parameters. one of the rules uses a free parameter that controls the accuracy of the resulted model, as well as its size. on average, the classification accuracy of the new algorithm is above 70% for the first best and above 80% for the first two best.
sparse decomposition over non-full-rank dictionaries. sparse decomposition (sd) of a signal on an overcomplete dictionary has recently attracted a lot of interest in signal processing and statistics, because of its potential application in many different areas including compressive sensing (cs). however, in the current literature, the dictionary matrix has generally been assumed to be of full-rank. in this paper, we consider non-full-rank dictionaries (which are not even necessarily overcomplete), and extend the definition of sd over these dictionaries. moreover, we present an approach which enables to use previously developed sd algorithms for this non-full-rank case. besides this general approach, for the special case of the smoothed ℓ0 (sl0) algorithm, we show that a slight modification of it covers automatically non-full-rank dictionaries.
overlay collaboration towards reduced bandwidth costs in multi-view streaming. delivering high-quality multiview video (mv) through internet is very challenging due to its excessive consumption on server bandwidth resources. existing solutions encode video contents independently for each view and deliver them separately in isolated view channels, without leveraging the features of mv and taking advantage of multiview video coding(mvc). to minimize the server bandwidth costs, we introduce a novel overlay collaboration framework that unifies all view channels to cooperate in delivering mv: i pictures in mvc are shared among them instead of requesting from server respectively; surplus resources of hotspot view channels are effectively utilized to help channels with insufficient resources, both of which contribute to remarkable reduction in server bandwidth costs. simulation experiments show that our method achieves more than 40% reduced bandwidth costs on server while maintaining scalability and resilience to user dynamics.
improved prosody generation by maximizing joint likelihood of state and longer units. the current state-of-art hmm-bsed tts can produce highly intelligible output speech and deliver a decent segmental quality. however, its prosody, especially at the phrase or sentence level, tends to be bland. the blandness of synthesized prosody is partially due to the fact that a state-based hmm is rather inadequate in modeling a global, hierarchical prosodic structure at a sentence or phrase level. in this study, the prosody of longer units are first modeled explicitly by appropriate parametric distributions. the resultant models are then integrated with the state-level baseline models to generate an optimal prosody by maximizing the joint likelihood of all, from state to longer, units. experimental results in both mandarin and english show consistent improvements over the state-based baseline system. the improvements are both objectively measurable and subjectively perceivable.
adaptive dereverberation of speech signals with speaker-position change detection. this paper proposes a method for adaptive speech dereverberation and speaker-position change detection, which have not previously been addressed. signal transmission channels in rooms are modeled as auto-regressive systems in individual frequency bands. the proposed method adaptively estimates the regression coefficients of this model, which are called room regression coefficients (rrcs). the proposed method has two distinguishing features: (1) the method is based on the weighted recursive least squares algorithm, which enables an efficient rrc-estimate update as well as a fast convergence rate; (2) the method detects changes in speaker position and so can quickly catch up with the sudden channel changes that such position changes cause. detection is realized by finding time frames where the power of dereverberated speech is anomalously amplified. experimental results showed that the proposed method attained convergence in 5 seconds and successfully detected changes in speaker position.
sparse boosting. we propose a boosting algorithm that seeks to minimize the adaboost exponential loss of a composite classifier using only a sparse set of base classifiers. the proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by adaboost. the algorithm can be viewed as a coordinate descent method for the l1-regularized adaboost exponential loss function.
improved lattice-based spoken document retrieval by directly learning from the evaluation measures. lattice-based approaches have been widely used in spoken document retrieval to handle the speech recognition uncertainty and errors. position specific posterior lattices (pspl) and confusion network (cn) are good examples. it is therefore interesting to derive improved model for spoken document retrieval by properly integrating different versions of lattice-based approaches in order to achieve better performance. in this paper we borrow the framework of ‘learning to rank’ from text document retrieval and try to integrate it into the scenario of lattice-based spoken document retrieval. two approaches are considered here, adarank and svm-map. with these approaches, we are able to learn and derived improved models using different versions of pspl/cn. preliminary experiments with broadcast news in mandarin chinese showed significant improvements.
parameter estimation of multidimensional nmr signals based on high-resolution subband analysis of 2d nmr projections. nmr spectroscopy is a powerful technique used in protein research for comprehensive functional characterizations, e.g. structure determination at atomic resolution. due to the molecular size (typically≫1000 atoms), protein nmr spectra contain a large number of signal frequencies. resolving these requires high-dimensional spectroscopy. however, when the number of frequency exceeds three, conventional approaches start to demand unrealistic long experiment time, and the data analysis becomes challenging. in this paper we explore a combination of novel methods: data from 5d nmr experiments are recorded as a series of 2d projections, which are then subjected to 2d subband filters and 2d ls-esprit for estimation of signal parameters. based on the relations established between 5d nmr signals and their 2d counterparts, projection spectroscopy allows to extract highly similar information as what would be found in conventional 5d nmr experiments; however, while the latter would require months of experiment time, the recording of all necessary projections can be accomplished within 1–2 days. preliminary results show the efficiency of the method with respect to accuracy and resolution of the parameter estimates as compared with conventional methods.
spatiotemporal latent semantic cues for moving people tracking. effective and robust visual tracking is one of the most important tasks for the intelligent visual surveillance. in this paper, we proposed a novel method for detecting and tracking moving people using the spatiotemporal latent semantic cues and the incremental eigenspace tracking techniques. during tracking process, the target appearance model is incrementally learned in low dimensional tensor eigenspace by adaptively updating the eigenbasis and sample mean. at the same time, the spatiotemporal latent semantic cues calibrate the estimation of tracking and detect new moving people coming in the same surveillance scene. experiment results show that with the calibration based on spatiotemporal latent semantic cues, the proposed method can track the moving people automatically and effectively.
low complexity azimuth and elevation estimation for arbitrary array configurations. in this paper we propose azimuth and elevation angle of arrival estimation algorithms for arbitrary array configurations. the proposed algorithms extend the polynomial rooting intersection for multidimensional estimation (prime) [1] and statistically efficient modified variable projection (mvp) [2] algorithms to arbitrary sensor array configurations without explicit knowledge of the steering vector. the proposed algorithms exploit the concept of manifold separation technique (mst) [3], [4]. thus, the data are processed in the element-space domain and are not subject to mapping errors. moreover, closed-form derivatives of the weighted subspace fitting (wsf) cost function are obtained, even for real-world arrays with imperfections, making the proposed mvp computationally attractive. the obtained estimates for both elevation and azimuth show an error variance close to the cramér-rao lower bound (crlb).
study of the quaternion lms and four-channel lms algorithms. the recently proposed quaternion least-mean-square (qlms) algorithm for adaptive filtering of three- and four-dimensional signals has been analysed in the context of multi-step ahead prediction. for rigour, the relationship between multichannel lms (mlms) and qlms is examined, and their differences are highlighted. this is achieved both in terms of the input-output relationship and in terms of the dynamics of weight updates. the convergence of qlms is investigated and stability bounds confirm that qlms and mlms are fundamentally different. simulations on both synthetic and real world multidimensional signals support the analysis.
application of voice conversion for cross-language rap singing transformation. voice conversion enables generation of a desired speaker's voice from audio recordings of another speaker. in this paper, we focus on a music application and describe the first steps towards generating voices of music celebrities using conventional voice conversion techniques. specifically, rap singing transformations from english to spanish are performed using parallel training material in english. weighted codebook mapping based voice conversion with two different alignment methods and temporal smoothing of the transformation filter are employed. the first aligner uses a hmm trained for each source recording to force-align the corresponding target recording. the second aligner employs speaker-independent hmms trained from a large number of speakers. additionally, a smoothing step is devised to reduce discontinuities and to improve performance. the results of subjective evaluations indicate that both aligners perform equivalently well. the proposed smoothing technique improves both similarity to target singer and quality significantly regardless of the alignment method.
distributed karhunen-loève transform with nested subspaces. a network in which sensors observe a common gaussian source is analyzed. using a fixed linear transform, each sensor compresses its high-dimensional observation into a low-dimensional representation. the latter is provided to a central decoder that reconstructs the source according to a mean squared error (mse) distortion metric. the distributed karhunen-loève transform (d-klt) has been shown to provide a (locally) optimal linear solution for compression at each sensor. while the d-klt achieves the lowest distortion linear reconstruction known, it does not maintain a nested subspace structure. in the case of ideal links to the decoder, this paper presents transforms that maintain nested subspaces, allowing the decoder to approximate a delay-limited source in an online fashion according to a desired sensor schedule. a distortion envelope for one distributed transform with nested subspace properties (d-nested-klt) is provided. in the case of i.i.d. noise to the decoder, under assumptions of power allocation over subspaces, it is also possible to achieve nested subspaces utilizing correlations between sensors' observations. results are applicable for data access over networks, and online information processing in sensor networks.
freshman design: a signal-processing approach. this paper describes development and evaluation of a freshman design course focusing on signal processing and communications, entitled “the digital world of multimedia.” the course covers basic concepts of sampling, frequency analysis, and filtering without calculus but with coverage of these topics in both audio and image processing. six labs provide collaborative, experimental learning as well as design opportunities. connections to modern technology are provided in guest lectures and reading assignments that leverage ieee magazines, and the topics provide opportunities for discussing the societal impact of signal processing technology.
complete characterization of the pareto boundary of interference-coupled wireless systems with power constraints - the log-convex case. in this paper we analyze the structure of certain power-constrained utility sets, based on the axiomatic framework of log-convex interference functions. log-convex interference functions contain convex and linear interference functions as a special case. we analyze the boundary of the set. it is shown how pareto optimality of boundary points depends on the interference coupling between the users. finally, we investigate feasible sets of signal-to-interference-plus-noise ratios for individual power constraints and a sum power constraint. we show certain properties that are desirable, e.g. in the context of cooperative game theory.
fast structure-preserving image retargeting. several different methods have been proposed for image/video retargeting while retaining the content. however, they sometimes produce some artifacts, such as ridge or structure twist. in this paper, we present a structure-preserving image resizing technique for the image retargeting applications. based on the warping-based retargeting technique proposed by wolf et al.[13], we propose an efficient and adaptive image resizing algorithm that preserves the content and image structure as best as possible. we first downsample the size of the original image by using bilinear interpolation. in order to preserve the content, we introduce the structure constraints derived from the line detection into the large linear system. then, the mapping matrices are enlarged to the original size by joint-bilateral upsampling and the resized image can be produced to preserve the content and structure as best as possible. most of the computation is on the low-resolution layer and therefore it can be very efficient. from our experiments, the proposed method can provide resized images with higher image quality and faster speed than that in [13].
using collective information in semi-supervised learning for speech recognition. training accurate acoustic models typically requires a large amount of transcribed data, which can be expensive to obtain. in this paper, we describe a novel semi-supervised learning algorithm for automatic speech recognition. the algorithm determines whether a hypothesized transcription should be used in the training by taking into consideration collective information from all utterances available instead of solely based on the confidence from that utterance itself. it estimates the expected entropy reduction each utterance and transcription pair may cause to the whole unlabeled dataset and choose the ones with the positive gains. we compare our algorithm with existing confidence-based semi-supervised learning algorithm and show that the former can consistently outperform the latter when the same amount of utterances is selected into the training set. we also indicate that our algorithm may determine the cutoff-point in a principled way by demonstrating that the point it finds is very close to the achievable peak point.
bandwidth adaptive hardware architecture of k-means clustering for intelligent video processing. k-means is a clustering algorithm that is widely applied in many fields, including pattern classification and multimedia analysis. due to real-time requirements and computational-cost constraints in embedded systems, it is necessary to accelerate k-means algorithm by hardware implementations in soc environments, where the bandwidth of the system bus is strictly limited. in this paper, a bandwidth adaptive hardware architecture of k-means clustering is proposed. experiments show that the proposed hardware has the maximum clock speed 400mhz with tsmc 90nm technology, and it can deal with feature vectors with different dimensions using five parallel modes to utilize the input bandwidth efficiently.
on the recovery of nonnegative sparse vectors from sparse measurements inspired by expanders. this paper studies compressed sensing for the recovery of non-negative sparse vectors from a smaller number of measurements than the ambient dimension of the unknown vector. we focus on measurement matrices that are sparse, i.e., have only a constant number of nonzero (and non-negative) entries in each column. for such measurement matrices we give a simple necessary and sufficient condition for l1 optimization to successfully recover the unknown vector. using a simple “perturbation” to the adjacency matrix of an unbalanced expander, we obtain simple closed form expressions for the threshold relating the ambient dimension n, number of measurements m and sparsity level k, for which l1 optimization is successful with overwhelming probability. simulation results suggest that the theoretical thresholds are fairly tight and demonstrate that the “perturbations” significantly improve the performance over a direct use of the adjacency matrix of an expander graph.