waashk/dblp huggingface dataset:
a game-theoretic framework for multi-user multimedia rate allocation. how to efficiently and fairly allocate data rate among different users is a key problem in the field of multiuser multimedia communication. however, most of the existing optimization-based methods, such as minimizing the weighted sum of the distortions or maximizing the weighted sum of the psnrs, have their weights heuristically determined. moreover, those approaches mainly focus on the efficiency issue while ignoring the fairness issue. in this paper, we address this problem by proposing a game-theoretic framework, in which the utility/payoff function of each user/player is jointly determined by the characteristic of the transmitted video sequence and the allocated bitrate. we show that with the proportional fairness criterion, the game has a unique nash equilibrium, according to which the controller can efficiently and fairly allocate the available network bandwidth to the users. finally, we show several experimental results on real video data to verify the proposed method.
combined image plus depth seam carving for multiview 3d images. multiview 3d displays have to multiplex a set of views on a single lcd panel. due to this, each view has to be downsampled by a considerable amount leading to loss of details. in this paper, we extend the seam carving technique for adaptive resizing of images. it is proposed that the depth information be used along with the image pixel intensity values for resizing. this results in better resized multiview images. it is clear from the results presented that the object structure is maintained when the proposed method is used as compared to vanilla seam carving.
conversation detection in ambient telephony. in some speech communication applications such as distributed hands-free telephony it is important that the system can detect the conversational state of a call. this cannot be performed by speech activity only because the captured signal may also contain conversation between two local people, or additional speech noise sources such as speech sounds from a radio or television. in this paper we compare known algorithms and introduce a new algorithm for the real-time detection of active conversation between an incoming caller and a local user. the method is based on the mutual information in speech activity, detection of back-channel speech activity, and statistics of overlapping speech. the proposed method gives over 90% accuracy within one minute observation period which is a clear improvement over the performance of earlier techniques.
distributed parameter estimation with selective cooperation. this paper proposes selective update and cooperation strategies for parameter estimation in distributed adaptive sensor networks. a set-membership filtering approach is employed that results in reduced complexity for updating parameter estimates at each network node, a significant reduction in information exchange between cooperating nodes, and an optimal strategy to obtain consensus estimates. the proposed strategies and the estimation algorithm offer a new way to explore cooperation in adaptive distributed sensor networks.
speech emotion recognition via a max-margin framework incorporating a loss function based on the watson and tellegen's emotion model. this paper considers a method for speech emotion recognition by a max-margin framework incorporating a loss function based on a well-known model called thewatson and tellegen's emotion model. each emotion is modeled by a single-state hidden markov model (hmm) that is trained by maximizing the minimum separation margin between emotions, and the margin is scaled by a loss function. the framework is optimized by the semi-definite programming. experiments were performed to evaluate the framework using the berlin database of emotional speech. the framework performed better than other conventional training criteria for hmm such as maximum likelihood estimation and maximum mutual information estimation.
a low-complexity noise estimation algorithm based on smoothing of noise power estimation and estimation bias correction. this paper presents a low-complexity algorithm for tracking the noise spectral variance of speech contaminated by non-stationary noise sources. the proposed algorithm is based upon a recursive refinement process in which each step of the algorithm expectation of the instantaneous noise power is calculated based on information from the incoming signal and the current estimated distribution parameters, and estimation of the distribution parameter is refined accordingly to incorporate the expectation results. a bias estimation correction method is also introduced in the algorithm to avoid estimation errors that may occur when there is a significant mismatch between the statistics of the input signal and the current estimated distribution parameters. the proposed algorithm is compared to the minimum statistics method and it is found that the proposed algorithm achieves similar or better performances for various noise conditions and snr settings. 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
voronoi cell shaping for feature selection with discrete hmms. in this paper, we introduce a novel vector quantization (vq) scheme for distributing the quantization error equally among the quantized dimensions. afterwards, the proposed vq scheme is used to perform feature selection in on-line handwritten whiteboard note recognition based on discrete hidden-markov-models (hmms). in an experimental section we show that the novel vq scheme derives feature sets which contain less than 50% features, enabling recognition with better performance at less computational costs. finally, the derived feature set is compared to the quantized features selected within a continuous hmm-based system: the features selected after quantization with the proposed vq scheme are proved to perform significantly better than those in the continuous system.
a regularized kernel-based approach to unsupervised audio segmentation. we introduce a regularized kernel-based rule for unsupervised change detection based on a simpler version of the recently proposed kernel fisher discriminant ratio. compared to other kernel-based change detectors found in the literature, the proposed test statistic is easier to compute and has a known asymptotic distribution which can effectively be used to set the false alarm rate a priori. this technique is applied for segmenting tracks from tv shows, both for segmentation into semantically homogeneous sections (applause, movie, music, etc.) and for speaker diarization within the speech sections. on these tasks, the proposed approach outperforms other kernel-based tests and is competitive with a standard hmm-based supervised alternative.
the effect of formant trajectories and phoneme durations on vowel intelligibility. we examined how much listeners can benefit from listening to “clear” (clr) speech compared to “conversational” (cnv) speech, both spoken at different speaking rates. vowel intelligibilities of four front vowels (/i:/, /i/, /e/, and /ei/) in background noise were measured with four speaking styles (cnv/slow, cnv, clr, and clr/fast). results showed only tense vowels of clr speech had a significant difference between cnv and clr speaking styles, after energy and f0 contour were normalized. we synthesized hybrid (hyb) speech whose formant features were equal to those of clr speech, while all other features were taken from cnv speech. primary conclusions from this study are (1) naturally-spoken fast clr speech was not as intelligible as clr speech, (2) enhancing formant frequencies to resemble those of clr speech was effective at improving vowel intelligibility, and (3) spectral tilt and formant bandwidths were not contributing factors to the clr speech benefit.
motion-based object segmentation using local background sprites. it is well known that video material with a static background allows easier segmentation than that with a moving background. one approach to segmentation of sequences with a moving background is to use preprocessing to create a static background, after which conventional background subtraction techniques can be used for segmenting foreground objects. it has been recently shown that global motion estimation and/or background sprite generation techniques are reliable. we propose a new background modeling technique for object segmentation using local background sprite generation. experimental results show the excellent performance of this new method compared to recent algorithms proposed.
network gossip algorithms. unlike the telephone network or the internet, many of the next generation networks are not engineered for the purpose of providing efficient communication between various networked entities. examples abound: sensor networks, peer-to-peer networks, mobile networks of vehicles and social networks. indeed, these emerging networks do require algorithms for communication, computation, or merely spreading information. for example, estimation algorithms in sensor networks, broadcasting news through a peer-to-peer network, or viral advertising in a social network. these networks lack infrastructure; they exhibit unpredictable dynamics and they face stringent resource constraints. therefore, algorithms operating within them need to be extremely simple, distributed, robust against network dynamics, and efficient in resource utilization. gossip algorithms, as the name suggests, are built upon a gossip or rumor style unreliable, asynchronous information exchange protocol. due to their immense simplicity and wide applicability, this class of algorithms has emerged as a canonical architectural solution for the next generation networks. this has led to exciting recent progress to understand the applicability as well as limitations of the gossip algorithms. in this survey, i will discuss some of these recent results on gossip network algorithms. the algorithmic results described here in a natural way bring together tools and techniques from markov chain theory, optimization, percolation, random graphs, spectral graph theory, and coding.
comparison of convex combination and affine combination of adaptive filters. in the area of combination of adaptive filters, two main approaches, namely convex and affine combinations have been introduced. in this article, the relation between these two approaches is investigated. first, the problem of obtaining optimal convex combination coefficients is formulated as the projection of the optimal affine combination weights to the unit simplex in a weighted inner product space. based on this formulation the closed form expressions for optimal combination weights and target mse levels are obtained for two and three branch cases.
on robustness of coupled adaptive filters. we provide a time domain analysis of the robustness and stability performance for coupled adaptive algorithms of gradient type. the considered coupling may occur inherently as well as by desire of the designer. the presented analyses focus on system identification. examples are presented to investigate convergence and steady-state behaviour by simulations which are compared to theory. in particular, the presented approach allows for a deeper understanding of cascaded adaptive filters in terms of robustness and l2-stability.
strategies for bit allocation reuse in audio transcoding. we study the reuse of the bit allocation information in audio transcoding by exploiting the similarity in subband audio coding schemes. we show that important information can be deduced to reduce the encoder complexity even if the two coders employ different psychoacoustic model. we give a case study on mpeg aac/dolby ac-3 transcoding. the proposed algorithms can be extended to other audio transcoding schemes.
people location and orientation tracking in multiple views. this paper presents a multi-view approach to the tracking of people location and orientation. to achieve efficient and accurate likelihood evaluation, a novel likelihood computation method is proposed. mixtures of gaussian (mog) are used to represent the color models of subjects. the scaled unscented transformation is used to project the mog color models onto the image plane to predict the color distribution for a motion sample. the efficacy of the proposed approach is demonstrated by experiment results obtained using real videos.
classification of movement-related single-trial meg data using adaptive spatial filter. in this paper, a method for extracting and classifying movement-related brain signals is proposed. a single-trial meg observation is first processed with a pre-whitening filter so that strong stationary interference is eliminated. next, a brain signal effective for classification is extracted using an adaptive spatial filter. the extracted signal is then classified with a support vector machine. from the experimental results, it is shown that the classification rate of 62.6 % is obtained for the brain signals related to the three types of hand movements (“scissors-paper-rock”).
detecting real life anger. acoustic anger detection in voice portals can help to enhance human computer interaction. a comprehensive voice portal data collection has been carried out and gives new insight on the nature of real life data. manual labeling revealed a high percentage of non-classifiable data. experiments with a statistical classifier indicate that, in contrast to pitch and energy related features, duration measures do not play an important role for this data while cepstral information does. also in a direct comparison between gaussian mixture models and support vector machines the latter gave better results.
a reduced-reference video structural similarity metric based on no-reference estimation of channel-induced distortion. the reduced-reference (rr) approximation of a full-reference (fr) video quality assessment method is a convenient way to build evaluation metrics which are both intrinsically well correlated with human judgments and feasible to implement in a network scenario, without the need to explore the perceptual significance of new video features through mean opinion score tests. in this paper, we propose a rr approximation of the video structural similarity index (vssim), a fr metric which is known to be well descriptive of the video quality perceived by users. we focus on the visual degradation produced by channel transmission errors: first, at the encoder, a small set of salient structural video features is assembled and transmitted through the rr channel to the end-user; then, at the decoder the feature vector is combined with a fine-granularity, no-reference estimate of the channel-induced distortion to produce the vssim approximation. by uniformly quantizing the feature vector and compressing it using a context-adaptive, variable length encoder, we show that good correlation coefficients with ground-truth vssim (ρ = 0.85) may be achieved spending, respectively, less than 12 and 27 kbps for a video sequence with cif or sd resolution.
perturbation and pitch normalization as enhancements to speaker recognition. this study proposes an approach to improving speaker recognition through the process of minute vocal tract length perturbation of training files, coupled with pitch normalization for both train and test data. the notion of perturbation as a method for improving the robustness of training data for supervised classification is taken from the field of optical character recognition, where distorting characters within a certain range has shown strong improvements across disparate conditions. this paper demonstrates that acoustic perturbation, in this case analysis, distortion, and resynthesis of vocal tract length for a given speaker, significantly improves speaker recognition when the resulting files are used to augment or replace the training data. a pitch length normalization technique is also discussed, which is combined with perturbation to improve open-set speaker recognition from an eer of 20% to 6.7%.
hierarchical clustering of neural data using linked-mixtures of hidden markov models for brain machine interfaces. in this paper, we build upon previous brain machine interface (bmi) signal processing models that require a-priori knowledge about the patient's arm kinematics. specifically, we propose an unsupervised hierarchical clustering model that attempts to discover both the interdependencies between neural channels and the self-organized clusters represented in the spatial-temporal neural data. given that bmis must work with disabled patients who lack arm kinematic information, the clustering work describe within this paper is very relevant for future bmis.
using the pearson correlation coefficient to develop an optimally weighted cross relation based blind simo identification algorithm. blind simo identification is challenging when additive noise is strong and for ill-conditioned/acoustic simo systems. a weighted cross relation (cr) algorithm presumably can be robust to noise but there lacks a practical way to define the weights. in this paper, the pearson correlation coefficient (pcc) is used to develop an optimally weighted cr algorithm, which is validated by simulations.
learning a music similarity measure on automatic annotations with application to playlist generation. this paper presents an approach to learn a better music similarity measure and presents an application to music playlist generation. different from previous work, in our approach, automatically detected music attributes are used to represent each song. a set of kernels is employed in similarity measure, with each kernel measuring on a subset of music attributes and having a different importance weight. in automatic music playlist generation, a ranking method is presented, which considers multiple seed songs and possible outlier seed. experiments show the effectiveness of the proposed approach, and the quality of the playlist generated based on automatic annotations is comparable to that based on manual annotations.
a dsk based simplified speech processing module for cochlear implant research. a simple yet flexible research module for laboratory experiments in evaluating performance of different speech processing strategies of cochlear implant is developed. it enables algorithm development and evaluation through a series of three sub-modules. software evaluation module provides a graphical environment to the researchers to develop new strategies as well as experiment with parametric variation of these strategies. evaluation is done by means of waveform analyzer and audible reconstruction of the synthesized sound. hardware evaluation module evaluates real-time operational performance of the algorithms in dsp environment. it highlights the practical bottlenecks of any algorithm when transferred from software to hardware domain. finally, real-time patient evaluation module allows performance evaluation of the developed strategy on actual cochlear implant patients. simple and easy to use guis enable clinicians and researchers to experiment with speech processing strategies with great ease and flexibility without requiring advanced programming skills.
averaged acoustic emission events for accurate damage localization. localizing micro cracks in critical components is crucial in the field of continuous structural health monitoring. in this paper, we utilize several signal processing and machine learning techniques such as hierarchical clustering and support vector machines (svm) to process multisensor acoustic emission (ae) data generated by the inception and propagation of cracks. we present preliminary laboratory results that explore the pairwise event correlation of ae waveforms generated in the process of controlled crack propagation, and use these characteristics for clustering ae. by averaging the ae events within each cluster obtained from hierarchical clustering, we compute super-acoustics with higher signal to noise ratio (snr) and use them in the second step of our analysis for calculating the time of arrival information (toa) for crack localization. we utilize a svm classifier to recognize the so called p-waves in the presence of noise by using features extracted from the frequency domain for accurate earliest arrival detection. preliminary results show that our method has the potential to be a component of a structural health monitoring system based on acoustic emissions for instance for bridges.
joint mimo radar waveform and receiving filter optimization. the concept of mimo (multiple-input multiple-output) radar allows each transmitting antenna element to transmit an arbitrary waveform. this provides extra degrees of freedom compared to the traditional transmit beamforming approach. it has been shown in the recent literature that mimo radar systems have many advantages. in this paper, we consider the joint optimization of waveforms and receiving filters in the mimo radar when the prior information of target and clutter are available. a novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. the proposed algorithm guarantees that the sinr performance improves in each iteration step. the numerical results show that the proposed methods have better sinr performances than existing design methods.
timing and frequency synchronization for ofdm based cooperative systems. in this paper, we investigate the timing and carrier frequency offset (cfo) synchronization problem in decode and forward cooperative systems operating over frequency selective channels. a training sequence which consists of one ofdm block having a tile structure in the frequency domain is proposed to perform synchronization. timing offsets are estimated using correlation-type algorithms. and since some subcarriers are nulled in the proposed tile structure, cfos are readily estimated using subspace-based methods. by judiciously designing the size of the tile, these algorithms are shown to have better performance, in terms of synchronization errors and bit error rate, than the computationally demanding sage algorithm.
cross-layer optimization of wireless fading ad-hoc networks. this paper develops near-optimal designs of wireless networks in the presence of fading. the novel approach optimizes jointly application level rates, routes, link capacities, power consumption and physical layer parameters. the physical layer is interference limited with terminals distributing their power budget among frequency tones, neighboring nodes and fading states. the present contribution builds on recent results establishing the optimality of layered architectures and develops physical layer resource allocation algorithms that are seamlessly integrated into layered architectures without loss of optimality.
affinely constrained online learning and its application to beamforming. this paper presents a novel method for incorporating a-priori affine constraints in online kernel-based learning tasks. the proposed technique elaborates the generic tool of projections to form a sequence of estimates in reproducing kernel hilbert spaces (rkhs). the method guarantees that the whole sequence of estimates lies in the given affine constraint set. to validate the algorithm, a beamforming task is considered. the numerical results show that the proposed frame provides with solutions in cases where the classical linear approach collapses, and forms proper beam-patterns as opposed to a recent unconstrained kernel-based regression method.