waashk/dblp huggingface dataset:
diversity analysis of antenna selection over frequency-selective mimo channels. antenna selection is a simple yet efficient technique to obtain diversity advantage from multi-input and multi-output (mimo) communication systems. without specifying system factors such as antenna selection criterion, coding and decoding, equalization, and channel statistics, we evaluate diversity orders of antenna selections over frequency-selective mimo channels. we show that the diversity order of any configuration approximately scales in the numbers of transmit and receive antennas. numerical simulations are provided to validate our analysis.
fast belief propagation process element for high-quality stereo estimation. belief propagation is a popular global optimization technique for many computer vision problems. however, it requires extensive computation due to the iterative message passing operations. in this paper, we present a new process element (pe) for efficient message construction. the efficiency is gained by exploiting the unique characteristics of the generalized potts model (truncated linear mode) of the smoothness term in the markov random field. for stereo estimation with l disparity values, the algorithm successfully reduces the computation from o(l2) to o(l) and retains the high throughput and low latency. compared with the direct message construction pe, our method achieves 87.14% computation saving and a 94.38% pe area reduction.
efficient blind decoding of mimo using sequential monte carlo. in this paper, blind decoding of ostbc based mimo systems using sequential monte-carlo methods is considered. similar receivers developed earlier have suffered from the high computational complexity. by introducing some simplification and approximation techniques, we present a new decoding algorithm that requires much less computation. in addition to that, an efficient interlaced maximum likelihood estimator approach is developed to blindly estimate the statistical parameters of the channel. while a comparative analysis on the computational complexity shows significant computational savings, the simulation results show that the performance of the proposed algorithm is not compromised.
automatic generation of maps of memory accesses for energy-aware memory management. many signal processing systems are synthesized to execute data-dominated applications. their behavior is described in a high-level programming language, where the code is typically organized in sequences of loop nests and the main data structures are multidimensional arrays. since data transfer and storage have a significant impact on both the system performance and the major cost parameters - power consumption and chip area, the designer must spend a significant effort during the system development process on the exploration of the memory subsystem in order to achieve a cost-optimized design. this paper focuses on the reduction of the dynamic energy consumption in the hierarchical memory subsystem of multidimensional signal processing systems, starting from the high-level behavioral specification of the application. the paper presents an algorithm which identifies those parts of arrays from a high-level specification that are intensely accessed (for read and/or write operations), whose storage on-chip yields the highest benefit in terms of dynamic energy consumption. tested on a two-layer memory hierarchy (scratch-pad and off-chip memories), this algorithm led to significant savings of energy in comparison to previous computation models.
on the use of bayesian modeling for predicting noise reduction performance. in speech enhancement applications, a validated metric of noise reduction performance is vital in the relative ranking of noise reduction algorithms and in enhancing the performance of a noise reduction algorithm. subjective scores of enhanced speech remain the yardstick for performance, but objective metrics that emulate subjective evaluations are preferred for cost- and time-effectiveness. in this paper, we analyze the performance of two objective methods for predicting the quality of enhanced speech. the first method employs the coherence-based speech intelligibility index, while the second method uses features derived from the moore - glasberg auditory model. in both cases, the features are mapped to a quality score using the bayesian modeling approach. results show that the combination of the auditory model-based feature set and the bayesian modeling provides the best performance in predicting the quality scores of enhanced speech.
spoken dialog strategy based on understanding graph search. we regarded information retrieval as a graph search problem and proposed several novel dialog strategies that can recover from misrecognition through a spoken dialog that traverses the graph. to recover from misrecognition without seeking confirmation, our system kept multiple understanding hypotheses at each turn and searched for a globally optimal hypothesis in the graph whose nodes express understanding states across user utterances in a whole dialog. as for a dialog strategy, we introduced a new criterion based on efficiency in information retrieval and consistency with understanding hypotheses to select an appropriate system response. using such criterion, the system removes the ambiguity so that users do not feel that a response that conflicts with the actual user intent is unnatural. we developed a spoken dialog system using these techniques and showed dialog examples in which misrecognition was naturally corrected. we also showed that our strategy was efficient in terms of the number of turns.
optimal distributed microphone phase estimation. this paper presents a minimum mean-square error spectral phase estimator for speech enhancement in the distributed multiple microphone scenario. the estimator uses gaussian models for both the speech and noise priors under the assumption of a diffuse incoherent noise field representing ambient noise in a widely dispersed microphone configuration. experiments demonstrate significant benefits of using the optimal multichannel phase estimator as compared to the noisy phase of a reference channel.
challenges and opportunities of obtaining performance from multi-core cpus and many-core gpus. multi-core processors represent a major development in computing technology. for example, intel® core™ 2 quad processors, ibm cell processors, and nvidia geforce 9800 gx2, are widely used. however, most applications struggle to make the best use of the power provided by many-core processors. easy-to-use software tools are hard to find. furthermore, it's not clear what changes need to be made to algorithms to fully utilize many-core cpus or gpus. in this paper, we try to offer a bird's eye view of the opportunities lying ahead in two folds: (1) software tools and (2) workload analysis. with good software tools and insightful workload analysis, software and algorithm developers can not only harness the power of many computing cores, but also innovate new algorithms that best utilize the many computing cores. new algorithms and applications are thus made possible with the computing power not available before.
musical audio semantic segmentation exploiting analysis of prominent spectral energy peaks and multi-feature refinement. in this paper we present a novel hierarchical and scalable three-stage algorithm to effectively perform musical audio semantic segmentation. in the first stage, the energy spectrum of the entire audio track is analyzed to find significant energy textures that may characterize different semantic segments; in the second and third stages, tonal and timbric features are used to refine the segmentation by moving or deleting segment boundaries. experimental results on a set of 58 songs show that our algorithm is able to attain good semantic segmentation just after the first step, with a precision of 64% and a recall of 96%. after second step the precision increases to 79%; the best precision result is obtained after the third step, where a value of 85% is reached. in this step the minimum average recall value of 92% is obtained.
background recovery from video sequences using motion parameters. this paper presents a novel scheme for extracting a still background occluded by a number of foreground objects, moving in different directions and velocities in a video sequence, such that every background pixel is exposed in at least one of the frames. each identified foreground object is decomposed into blocks. the proposed scheme is able to efficiently estimate, for each foreground block, a source frame from which the occluded background pixels can be extracted. the pixels of the identified source frames are used to populate the co-located occluded pixels in the initial frame. the efficacy and the simplicity of the algorithm lie in its capacity to recover the background directly from the estimated source frames instead of performing a foreground-background classification for every frame. the proposed algorithm is robust to variations in lighting and is effective in removing both rigid and deformable foreground objects. simulation results are presented to illustrate the performance of the proposed scheme.
evaluating digital audio authenticity with spectral distances and enf phase change. this paper discusses the use of spectral distances obtained from adaptive filters employed as linear predictors and phase change of the electric network frequency to evaluate digital audio authenticity. an authenticity evaluation may be of paramount importance for audio forensics and may help a criminalistic laboratory when dealing with audio evidence in a court of law. we present in this paper a theoretical background of the proposed scheme and show results with digitally edited speech.
extensions of absolute discounting (kneser-ney method). the problem of estimating the parameters of an n-gram language model is a typical problem of estimating small probabilities. so far, two methods have been proposed and used to handle this problem: 1. the empirical bayes method resulting in the turing-good estimates. theses estimates do not have any constraints and tend to be very noisy. 2. discounting models like absolute (or linear) discounting. the discounting models are heavily constrained and typically have only a single free parameter. both methods can be formulated in a leaving-one-out framework. in this paper, we study methods that lie between these two extremes. we design models with various types of constraints and derive efficient algorithms for estimating the parameters of these models. we propose two novel types of constraints or models: interval constraints and the exact extended kneser-ney model. the proposed methods are implemented and applied to language modelling in order to compare the methods in terms of perplexities. the results show that the new constrained methods outperform other unconstrained methods.
lossless data hiding for electronic ink. this paper presents a novel lossless data hiding technique for electronic inks. the proposed algorithm first computes the analytical ink-curve function for each stroke as a set of smoothly concatenated cubic bezier curves. during embedding, a set of data carrier points on the ink-curve are computed, perturbed and inserted back into the original point array together with some marker points. in extraction, data carrier points are identified and compared with their original positions in order to decode the secret message. through experiments, we demonstrate that significant mount of secret message can be embedded and the ink-curve computed from the marked ink data closely resembles the original ink-curve.
multilinear generalization of common spatial pattern. the common spatial patterns (csp) algorithm has been widely used in eeg classification and brain computer interface (bci). in this paper, we propose a multilinear formulation of the csp, termed as tensorcsp or common tensor discriminant analysis (ctda) for high-order tensor data. as a natural extension of csp, the proposed algorithm uses the analogous optimization criteria in csp and a new framework for simultaneous optimization of projection matrices on each mode based on tensor analysis theory is developed. experimental results demonstrate that our proposed algorithm is able to improve classification accuracy of multi-class motor imagery eeg.
exponential error bounds for binary detection using arbitrary binary sensors and an all-purpose fusion rule in wireless sensor networks. wireless sensor networks are considered in which sensors convey binary decisions over fading channels to a common fusion center. the fusion center first takes each received signal and makes an estimate of the transmitted bit. the average of the estimated bits is compared to a threshold to make a global decision. exponential error bounds are derived that allow one to trade off signal-to-noise ratio versus the number of sensors to achieve desired average error levels. an attractive feature of the bounds is that they do not require exact knowledge of the wireless channel statistics; approximations are sufficient.
periodic event detection and recognition in video. periodicity attracts special attention in human cognition. hence it is important to consider that in automatic analysis of motion events. this paper presents a method for representing periodic events with which events can be compared irrespective of their duration. the effectiveness of such a representation is verified with event classification.
multipath diversity and coding gains of cyclic-prefixed single carrier systems. the multipath diversity and coding gain metrics for cyclic-prefixed single-carrier (sc-cp) systems, which characterize the bit error rate (ber) at high snr, have not been carefully studied in the literature. we first show that, unlike ofdm, the diversity and coding gains for sc-cp are data-realization-dependent. then, we show that there is a signal-tonoise ratio (snr) threshold beyond which the dominant diversity order starts deviating from the maximum diversity order to eventually reduce to one at higher snrs. using the averaged pairwise probability, we derive an analytical expression for this snr threshold. the latter is shown to increase with the block length and to be unrealistically high for moderate/high block lengths. comparisons of sc-cp with rotated constellations and zero-padded sc systems are also provided.
psychoacoustically constrained and distortion minimized speech enhancement algorithm. a psychoacoustically constrained and distortion minimized speech enhancement algorithm is considered. in general, noise reduction leads to speech distortion, and thus, the goal of an enhancement algorithm should reduce noise and speech distortion so that both are inaudible. in this paper, a constrained optimization problem is formulated so that speech distortion is minimized while distortion that includes residual noise and speech distortion is kept below the masking threshold of the clean speech. experimental results show that the algorithm considered in this paper outperforms some of the more popular algorithms in terms of improvement in segmental signal-to-noise ratio (segsnr) and spectral distance (sd).
data-driven lexicon expansion for mandarin broadcast news and conversation speech recognition. we present a data-driven framework for expanding the lexicon to improve mandarin broadcast news and conversation speech recognition. the lexicon expansion includes the generation of pronunciation variants for frequent words and vocabulary augmentation with new words and phrases derived from the training data. to learn multiple pronunciations, we first generate all possible pronunciation candidates for a word from its character pronunciation network. the top pronunciation variants are then selected from forced alignment statistics. to augment the acoustic vocabulary, we propose an efficient algorithm that derives new words based on n-gram statistics. experiments show that a dictionary expanded in this manner yields significant improvements on a mandarin broadcast speech recognition task.
quantifying morphology changes in time series data with skew. this paper examines strategies to quantify differences in the morphology of time series while accounting for time skew in the observed data. we adapt four measures originally designed for signal shape comparison: dynamic time-warping (dtw), earth mover's distance (emd), fréchet distance (fd), and hausdorff distance (hd). these morphology difference metrics on time series are compared in discriminative power and noise resistance on ecg signals as well as on a synthetic dataset. we use data from our experiments to shed light on the relative strengths of the methods.
blind subspace-based channel estimation using the em algorithm. we propose an application of the expectation-maximization (em) algorithm to the problem of blind estimation of single-input multiple-output (simo), finite-impulse-response (fir) channels. we first assume gaussian input to formulate an em-based estimation of the signal subspace of the output covariance matrix. this gaussian assumption allows us to utilize knowledge from em-based probabilistic principle component analysis (p-pca). next, we show that the equilibrium point of the em iteration equations is reached without the gaussian assumption, which suggests usage of non-gaussian communication input signals. the estimated signal subspace is then utilized to identify the channels. in principle, the proposed method yields the same channel estimates as the widely-known subspace method, but is computationally more efficient. in addition, unlike typical em applications, the proposed scheme is free from cumbersome parameter initialization issue, which greatly increases flexibility of the proposed scheme.
a compact microphone array system with spatial post-filtering for automotive applications. compact microphone arrays allow for directional filtering with a minimum of installation space. they are therefore particularly suitable for automotive applications. typically, compact arrays are realized as differential arrays or filter-and-sum beamformers which both show limited performance in terms of directivity. in this contribution we present a novel system for directional filtering for compact arrays. this system consists of two closely spaced microphones and incorporates an adaptive beamformer as well as a spatial post-filter which is designed to suppress non-stationary noise.
a physical approach to moving cast shadow detection. this paper presents a physics-based approach capable of detecting cast shadows in video sequence effectively. we develop a new physical model of cast shadows without making prior assumption of the spectral power distribution (spd) of the light sources and ambient illumination in the scene. the background appearance variation caused by cast shadows is characterized as the interaction of the blocked light sources and the background surface reflectance. we then take advantage of the statistical prevalence of cast shadows to learn and update the shadow model parameters using the gaussian mixture model (gmm) over time. the proposed algorithm is completely unsupervised and can adapt to specific environment with complex illumination condition as well as changing shadow conditions. experimental results on three challenging sequences demonstrate the effectiveness of the proposed method.
multidimensional signal reconstruction from multichannel acquisition. we provide an analysis of the algorithms necessary for the optimal use of multidimensional signal reconstruction from multichannel acquisition. first, we provide computable conditions to test the matrix invertibility and propose algorithms to find a particular inverse. second, we determine the existence of perfect reconstruction systems for given fir analysis filters with some sampling matrices and some fir synthesis polyphase matrices. then, we present the development of an efficient algorithm designed to find a sampling matrix with maximum sampling rate and fir synthesis polyphase matrix for given fir analysis filters so that the system provides a perfect reconstruction. once a particular synthesis matrix is found, we can characterize all synthesis matrices and find an optimal one according to a design criterion.
robust speech dereverberation based on non-negativity and sparse nature of speech spectrograms. this paper presents a blind dereverberation method designed to recover the subband envelope of an original speech signal from its reverberant version. the problem is formulated as a blind deconvolution problem with non-negative constraints, regularized by the sparse nature of speech spectrograms. we derive an iterative algorithm for its optimization, which can be seen as a special case of the non-negative matrix factor deconvolution. we confirmed through experiments that the algorithm is fast and robust to speaker movement.
energy-efficient graph-based wavelets for distributed coding in wireless sensor networks. this work presents a class of unidirectional lifting-based wavelet transforms for an arbitrary communication graph in a wireless sensor network. these transforms are unidirectional in the sense that they are computed as data is forwarded towards the sink on a routing tree. we derive a set of conditions under which a lifting transform is unidirectional, then find the full set of those transforms. among this set, we construct a unidirectional transform that allows nodes to transform their own data using data forwarded to them from their descendants in the tree and data broadcasted to them from their neighbors not in the tree. this provides a higher quality data representation than existing methods for a fixed communication cost.
detection and classification of liquid explosives using nmr. in this work, we present a novel method for non-invasive identification of liquids, for instance to allow for the detection of liquid explosives at airports or border controls. the approach is based on a nuclear magnetic resonance technique with an inhomogeneous magnetic field, forming estimates of the liquid's spin-spin relaxation time, t2, and diffusion constant, d, thereby allowing for a unique classification of the liquid. the proposed detectors are evaluated using both simulated and measured data sets.
on the phonetic information in ultrasonic microphone signals. we study the phonetic information in the signal from an ultrasonic “microphone”, a device that emits an ultrasonic wave toward a speaker and receives the reflected, doppler-shifted signal. this can be used in addition to audio to improve automatic speech recognition. this work is an effort to better understand the ultrasonic signal, and potentially to determine a set of natural sub-word units. we present classification and clustering experiments on cvc and vcv sequences in speaker-dependent and multi-speaker settings. using a set of ultrasonic spectral features and diagonal gaussian models, it is possible to distinguish all consonants and most vowels. when clustering the confusion data, the consonant clusters mostly correspond to places and manners of articulation; the vowel data roughly clusters into high, low, and rounded vowels.