waashk/dblp huggingface dataset:
the effectiveness of histogram equalization on environmental model adaptation. in this paper, we introduce a new histogram equalization-based environmental model adaptation method for robust speech recognition in noise environments. the proposed method adapts initially-trained acoustic mean models of a speech recognizer into the environmentally matched models. the covariance models are adapted by using utterance-level local covariance matrices. we performed a series of experiments based on the aurora2 framework to examine the effectiveness of the proposed environmental model adaptation technique. in both clean and multi-condition trainings, the proposed approach achieved substantial performance improvements over the baseline speech recognizers.
ordering random object poses. complete or partial three-dimensional reconstruction of objects from multiple angle-views, or poses, is important in several applications such as photogrammetry, machine vision, and computer-aided design. knowledge of the pose angles and their proper ordering are required for accurate reconstruction. when these multiple angle images are acquired in random order and the angle of view information is not available the poses have to be put into proper order. this work presents an approach based on principal component analysis (pca) for automatic ordering of random object poses. a measure based on local curvature and correlation of the estimated pose trajectory in a multidimensional manifold is also developed to assess confidence in the ordering. in addition to providing a degree of confidence for pose ordering with single cameras, this measure enhances the pose estimation accuracy in double and multiple camera systems by providing a basis for camera selection for different poses. the paper presents theoretical development and experimental results.
camera motion influence on dynamic saliency central bias. saliency models have been extensively studied for static images and the focus is now on moving images. there is a central bias in both cases that is emphasized in the dynamic case. one aspect in this latter is the camera motion that influences the scene interpretation. the movie director exploits this motion to make the observer focus on the targeted object which is often in the center of the scene. this aspect is not taken into account in current saliency dynamic models. in this paper, we study the camera motion influence on the gaze distribution in order to include it in a new saliency model. observers' gazes are recorded with an eye tracker, camera motions (e.g. tracking, zoom…) are calculated thanks to a polynomial projection of the motion field and the motion influence is statistically tested on the recorded gazes.
testing fractal connectivity in multivariate long memory processes. within the framework of long memory multivariate processes, fractal connectivity is a particular model, in which the low frequencies (coarse scales) of the interspectrum of each pair of process components are determined by the autospectra of the components. the underlying intuition is that long memories in each components are likely to arise from a same and single mechanism. the present contribution aims at defining and characterizing a statistical procedure for testing actual fractal connectivity amongst data. the test is based on fisher's z transform and pearson correlation coefficient, and anchored in a wavelet framework. its performance are analyzed theoretically and validated on synthetic data. its usefulness is illustrated on the analysis of internet traffic packet and byte count time series.
a fast asymptotically efficient algorithm for blind separation of a linear mixture of block-wise stationary autoregressive processes. we propose a novel blind source separation algorithm called block autoregressive blind identification (barbi). the algorithm is asymptotically efficient in separation of instantaneous linear mixtures of blockwise stationary gaussian autoregressive processes. a novel closed-form formula is derived for a cramér rao lower bound on elements of the corresponding interference-to-signal ratio (isr) matrix. this theoretical isr matrix can serve as an estimate of the separation performance on the particular data. in simulations, the algorithm is shown to be applicable in blind separation of a linear mixture of speech signals.
jointly optimized mode decisions in redundant video streaming. this paper revisits the problem of source-channel coding for error-resilient video streaming, using redundant encoding. we propose a new method to jointly optimize the macroblock (mb) mode in both the primary and the secondary, redundant encoding. encoding decisions are based on end-to-end distortion using the rope estimate, and are adaptive at the mb level. further, we present a reduced complexity approach to redundant encoding. the proposed methods are general in nature, and could be implemented on top of any (hybrid) video codec. an example implementation employs the redundant slice mechanism of h.264 (jm 13.2). simulation results show significant performance gains over conventional methods such as fixed redundant encoding schemes or non-redundant optimal mb mode selection.
sensor subset selection for surface electromyograpy based speech recognition. the authors previously reported speaker-dependent automatic speech recognition accuracy for isolated words using eleven surface-electromyographic (semg) sensors in fixed recording locations on the face and neck. the original array of sensors was chosen to ensure ample coverage of the muscle groups known to contribute to articulation during speech production. in this paper we systematically analyzed speech recognition performance from sensor subsets with the goal of reducing the number of sensors needed and finding the best combination of sensor locations to achieve word recognition rates comparable to the full set. we evaluated each of the different possible subsets by its mean word recognition rate across nine speakers using hmm modeling of mfcc and co-activation features derived from the subset of sensor signals. we show empirically that five sensors are sufficient to achieve a recognition rate to within a half a percentage point of that obtainable from the full set of sensors.
a new scheme for synchronization of inactive nodes in a sender-receiver protocol. this paper targets the problem of clock synchronization for a set of receivers lying within the broadcast range of two nodes implementing a general sender-receiver protocol using a wireless channel. the maximum likelihood estimate of the clock offset of the inactive node (and mean link delay) hearing the broadcasts from both the master and slave nodes was derived in [1] assuming symmetric exponential link delays. in this paper, the minimum variance unbiased estimate for the clock offset of such nodes is derived by applying the rao-blackwell-lehmann-scheffé theorem. the result is important in the realm of wireless sensor networks, where a tight network synchronization along with a conservative energy utilization plays a major role in network performance.
an improvement of subgradient projection operator by composing monotonic functions. the subgradient projection operator has been utilized as a computationally efficient tool not only for suppression but also for minimization of convex functions in many applications. in this paper, we propose a systematic scheme to improve significantly the monotone approximation ability, of the subgradient projection, to the level set of a convex function. the proposed scheme is based on a simple observation: the level set of a convex function does not change by composing any zero-crossing monotonically increasing function. a numerical example demonstrates the effectiveness of the proposed scheme in an application to a simple boosting problem.
latent semantic retrieval of personal photos with sparse user annotation by fused image/speech/text features. while users prefer high-level semantic photo descriptions (e.g., who, what, when, where), we wish to minimize the need to annotate photos using such descriptions by the user. we propose a latent semantic personal photo retrieval approach using fused image/speech/text features. we use low-level image features to derive relatoionships among sparsely annotated photos, and probabilistic latent semantic analysis (plsa) models based on fused image/speech/text features to analyze photo “topics”. we then retrieve the photos using text or speech queries of simple high-level semantic words only. in preliminary experiments, while only 10% of the photos were manually annotated, the photos could be well retrieved with very encouraging results.
hdr image compression using optimized tone mapping model. in this paper, we propose a coding algorithm for high dynamic range images (hdri). our encoder applies a tone mapping model based on scaled μ-lawencoding, followed by a conventional low dynamic range image (ldri) encoder. the tone mapping model is designed to minimize the difference between the tone mappedhdri and its ldr version. by virtue of the nature of the model, not only the quality of the hdri but also the one of ldri are improved, compared with a state of the art in conventional hdri compression. furthermore the error caused by our tone mapping model encoding is theoretically analyzed.
spatio-temporal adaptive detector in non-homogeneous and low-rank clutter. reducing the number of secondary data used to estimate the clutter covariance matrix (ccm) for space time adaptive processing (stap) techniques is still an active research topic. low rank ccm estimates have already been proposed but only for homogeneous and gaussian clutter. we propose in this paper to extend the low-rank ccm methods for heterogeneous and/or non-gaussian clutter. we derive a new detector based on low-rank techniques and exploiting properties of the normalized sample covariance matrix (nscm). this detector is shown to exhibit a smaller snr loss than classical stap detectors. moreover, the new detector has a texture-cfar property with respect to non-gaussian sirv model and has more robust behavior when some targets are present in the secondary data. we also give experimental comparison results between the classical stap detectors and the new one for stap data.
range image quality assessment by structural similarity. we propose a new quality metric for range images that is based on the multi-scale structural similarity (ms-ssim) index. the new metric operates in a manner to ssim but allows for special handling of missing data. we demonstrate its utility by reevaluating the set of stereo algorithms evaluated in the middlebury stereo vision page http://vision.middlebury.edu/stereo/. the new algorithm which we term range ssim (r-ssim) index possesses features that make it an attractive choice for assessing the quality of range images.
improved maximum likelihood location estimation accuracy in wireless sensor networks using the cross-entropy method. this paper considers the problem of target location estimation in a wireless sensor network based on ieee 802.15.4 radio signals and proposes a novel implementation of the maximum likelihood (ml) location estimator based on the cross-entropy (ce) method. in the proposed ce method, the ml criterion is translated into a stochastic approximation problem which can be solved effectively. simulation results comparing the performance of a ml target estimation scheme employing the conventional newton method and the conjugate gradient method are presented. the simulation results show that the proposed ce method provides higher location estimation accuracy throughout the sensor field1 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
a new bitplane coder for scalable transform audio coding. this paper proposes a new bit plane coding method for signed integer sequences. this method consists in mapping successive bit planes onto quinary symbols (+, “, 0, 1, eop), where the symbol “eop” stands for “end of plane”, and applying arithmetic coding. sign bits are efficiently coded in combination with the corresponding most significant bit of non-zero integers. moreover, bit planes are scanned and coded in a non-sequential manner to exploit the correlation between successive planes. results for conversational transform coding of wideband speech and audio signals - sampled at 16 khz - show that the performance/complexity of the proposed bitplane coder is near equivalent to non-embedded coding (stack-run coding), while offering additional flexibility (bitstream scalability).
dense disparity estimation in a multi-view distributed video coding system. distributed video coding (dvc) is a recent paradigm which aims at transferring part of the coding complexity fromthe encoder to the decoder. the performance of such a coding scheme strongly depends on the capacity to estimate correlation at the decoder and, consequently, on the side information quality. in this paper we consider a multi-view dvc framework and propose a very efficient dense disparity estimation technique for side information construction, based on a variational formulation. the simulation results show that our approach clearly outperforms the existing methods for inter-view side-information generation.
rational canonical form of polyphase matrices with applications to designing paraunitary filter banks. in this paper we consider the rational canonical form of arbitrary polyphase matrices and use it to derive a simple implementation of paraunitary filter banks (pufbs) based on a cascade of elementary building blocks. furthermore, this decomposition is shown to be easily extendable to include a large class of perfect reconstruction filter banks (prfbs) and can be especially useful for deriving the initial condition of pufb design algorithms.
automatic musical meter detection. a method that automatically estimates the metrical structure of a piece of music is presented. the approach is based on the generation of a beat similarity matrix, which provides information about the similarity between any two beats of a piece of music. the repetitive structure of most music is exploited by processing the beat similarity matrix in order to identify similar patterns of beats in different parts of a piece. this principle proves to be equally effective for the detection of both duple and triple meters as well as complex meters. the use of beat positions and dynamic programming techniques allows tracking similar musical patterns formed by beats with moderate tempo deviations. the robustness of the presented approach is reflected by the results presented, where 361 songs are used in order to compare the presented approach against the use of the autocorrelation function in existing state of the art meter detection methods. 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
multi-task classification with infinite local experts. we propose a multi-task learning (mtl) framework for non-linear classification, based on an infinite set of local experts in feature space. the usage of local experts enables sharing at the expert-level, encouraging the borrowing of information even if tasks are similar only in subregions of feature space. a kernel stick-breaking process (ksbp) prior is imposed on the underlying distribution of class labels, so that the number of experts is inferred in the posterior and thus model selection issues are avoided. the mtl is implemented by imposing a dirichlet process (dp) prior on a layer above the task-dependent ksbps.
multimodal blind source separation for moving sources. a novel multimodal approach is proposed to solve the problem of blind source separation (bss) of moving sources. the challenge of bss for moving sources is that the mixing filters are time varying, thus the unmixing filters should also be time varying, which are difficult to track in real time. in the proposed approach, the visual modality is utilized to facilitate the separation for both stationary and moving sources. the movement of the sources is detected by a 3-d tracker based on particle filtering. the full bss solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary, a frequency domain bss algorithm is implemented with an initialization derived from the visual information. once the sources are moving, a beamforming algorithm is used to perform real time speech enhancement and provide separation of the sources. experimental results show that by utilizing the visual modality, the proposed algorithm can not only improve the performance of the bss algorithm and mitigate the permutation problem for stationary sources, but also provide a good bss performance for moving sources in a low reverberant environment.
chinese intonation assessment using sev features. intonation assessment is an important part of chinese call system. nowadays, most systems use the correlation and rmse features to assess the quality of the intonation of a given speech. as correlation and rmse assign unoptimized weights to different degrees of mismatching errors, they may lead to performance degradation. in this paper, we propose a new feature called sorted error vector (sev) for intonation assessment. the basic idea is to calculate mismatching quantities, sort them with ascending order, and then re-sample them to a k-points vector. this feature has four benefits: first, it is text-length independent; second, weights are let to train by classifiers; third, the relationship between the errors and the final results is not limited to any assumption; fourth, sev is not sensitive to the performance of different pitch extracting algorithms. experiments show that no matter in which case, sev feature performs the best.
enhanced spatially interleaved dvc using diversity and selective feedback. systems with cheap/simple/power efficient encoders but complex decoders make applications such as low cost, low power remote sensors practical. bandwidth considerations however are still an issue and compression efficiency has to remain high. in this paper, we present a distributed video codec (dvc) that we are developing with the aim of achieving such a low power paradigm at the cost of only a small compression performance deficit relative to the current state of the art, h.264. the proposed system employs spatial interleaving of key and wyner-ziv data which allows efficient side information (si) generation through block-based error concealment, a gray code that increases the accuracy of bit probability estimation, and a diversity scheme that produces more reliable results by exploiting multiple si generated data. simulation results show an improvement of the proposed scheme over h.264 intra coding of up to 1.5 db. we additionally propose two mechanisms for selective parity bit feedback requests that can fizrther reduce the wz bitrate by up to 15%.
impact of novel sources on content-based image and video retrieval. the problem of content-based image and video retrieval with textual queries is often posed as that of visual concept classification, where classifiers for a set of predetermined visual concepts are trained using a set of manually annotated images. such a formulation implicitly assumes that the training data has similar distributional characteristics as that of the data which need to be indexed. in this paper we demonstrate empirically that even within the relatively narrow domain of news videos collected from a variety of news programs and broadcasters, the assumption of distributional similarity of visual features does not hold across programs from different broadcasters. this is manifested in considerable degradation of ranked retrieval performance on novel sources. we observe that concepts whose spatial locations remain relatively fixed between various sources are also more robust to source mismatches, and vice versa. we also show that a simple averaging of multiple visual detectors is more robust than any of the individual detectors. furthermore, we show that for certain sources using only 20% of the available annotated data can bridge roughly 80% of the performance drop, while others can require larger amounts of annotated data.
joint carrier frequency offset and channel estimation in ofdm based non-regenerative wireless relay networks. cooperative relaying is an effective approach to combat wireless fading. however, the reliability enhancements depend strongly on the accuracy of the carrier frequency offset (cfo) compensation and channel estimation algorithms. in this paper, we show that cfo compensation at relay is necessary in non-regenerative ofdm based wireless relay networks with relays employing space-time coding. a simple training scheme available for estimating space-time channel is exploited to estimate the cfo at the relay. maximum likelihood (ml) and least squares (ls) based joint cfo and channel estimators are constructed for estimating the cfo and the product channel (effective channel from source to destination) at destination. for non-regenerative ofdm based wireless relays, we prove that the ls based estimator is equivalent to the ml based estimator. theoretical mean square error for the product channel estimator is also derived.
skellamshrink: poisson intensity estimation for vector-valued data. owing to the stochastic nature of discrete processes such as photon counts in imaging, a variety of real-world data are well modeled as poisson random variables whose means are in turn proportional to an underlying vector-valued signal of interest. certain wavelet and filterbank transform coefficients corresponding to measurements of this type are distributed as sums and differences of poisson counts, taking in the simplest case the so-called skellam distribution. we show that a skellam mean estimator provides a poisson intensity estimation method based on shrinkage of filterbank coefficients, and a means of estimating the risk of any skellam mean estimator is derived in closed form under a frequentist model.
refactoring acoustic models using variational density approximation. in model-based pattern recognition it is often useful to change the structure, or refactor, a model. for example, we may wish to find a gaussian mixture model (gmm) with fewer components that best approximates a reference model. one application for this arises in speech recognition, where a variety of model size requirements exists for different platforms. since the target size may not be known a priori, one strategy is to train a complex model and subsequently derive models of lower complexity. we present methods for reducing model size without training data, following two strategies: gmm-approximation and gaussian clustering based on divergences. a variational expectation-maximization algorithm is derived that unifies these two approaches. the resulting algorithms reduce the model size by 50% with less than 4% increase in error rate relative to the same-sized model trained on data. in fact, for up to 35% reduction in size, the algorithms can improve accuracy relative to baseline.
using context information and local feature points in face clustering for consumer photos. we introduce local feature points to achieve face clustering for consumer photos. after combining eigenfaces with context information like clothes, we further investigate the usage of local feature points to match face images. the relationships between face images are constructed by feature matching and then described as a graph. outliers in the results of preliminary clustering are detected and are re-clustered according to matching characteristics. we report complete performance comparison for different datasets and show that the proposed method has superior performance than conventional approaches.