waashk/dblp huggingface dataset:
aspect modeling of parsed representation for image retrieval. a probabilistic framework based on a universal source coding for content-based image retrieval is proposed. by a multidimensional incremental parsing technique, which is an extension of the lempel-ziv incremental parsing algorithm, a given image is parsed into a number of variable-size rectangular blocks, called parsed representations. to achieve a semantically relevant pattern matching, we introduce a new similarity measure from the first- and second-order statistics of given image patches. once the occurrence patterns of images in the corpus are analyzed, the term-document joint distribution is estimated by an aspect modeling technique under the assumption of latent aspects. to compare the performance of the proposed image retrieval framework based on the parsed representations, we implement a benchmark system based on the fixed-shape block representations trained by vector quantization. in addition to these two systems, we bring two content-based image retrieval systems into the performance evaluation. the experimental results on a database of 20,000 natural scene images demonstrate that the proposed image retrieval system significantly outperforms other existing and the benchmark systems.
multiview video compression and streaming based on predicted viewer position. recent technological advances have made possible a number of new applications in the area of 3d video. one of the enabling technologies for many of these 3d applications is multiview video coding, which has received significant attention in the last several years. however, the fundamental need of multiview coding for applications like immersive tele-conferencing has not been addressed. in this paper we define the boundaries of the problem, and show how a simple algorithm can yield gains of up to 2× reduction in bitrate with similar psnr in the synthesized view. our algorithm is based on using an estimate of the viewer position to compute the expected contribution of each pixel to the synthesized view, and encoding each macroblock of each camera views with quality proportional to the likelihood that the pixel will be used in the synthetic image.
probablistic modelling of f0 in unvoiced regions in hmm based speech synthesis. hmm based synthesis has attracted great interest due to its compact and flexible modelling of spectral and prosodic parameters. in this approach, short term spectra, fundamental frequency (f0) and duration are simultaneously modelled by multi-stream hmms. however, since f0 values in unvoiced regions are normally considered as undefined, it is difficult to use standard hmms for f0 modelling. the currently preferred solution to this is to use a multi-space distribution hmm (msdhmm) in which discrete distributions are used for modelling the voiced/unvoiced decision and continuous gaussian distributions are used for modelling the f0 values within the voiced regions. however, the assumption of undefined unvoiced f0 regions and the special structure of the msdhmm lead to limitations in the accurate modelling of f0 patterns. in this paper an alternative is explored whereby unvoiced f0 values are assumed to exist and are modelled within the standard hmm framework using a globally tied distribution (gtd). subjective evaluations show that these regular hmms with gtd can produce significant improvements in the naturalness of the synthesised speech compared to the msdhmm, and furthermore, the method is insensitive to the exact method used for unvoiced f0 generation.
modeling of contours in wavelet domain for generalized lifting image compression. this paper introduces the design of context-based models of contours in the wavelet domain, which are used to construct generalized lifting (gl) mappings for image compression. the gl context-based mapping may significantly reduce the signal energy and the resulting bitrate. here, we propose a strategy to define a reduced set of structured models to design the gl. the models capture the contour structures and are contrast-invariant. initial experimental results applying the strategy on a wavelet subband exhibit potential gains. iterations of the gl scheme as well as an adaptive entropy coding strategy may increase the coding gain.
unsupervised pronunciation validation. this paper addresses selecting between candidate pronunciations for out-of-vocabulary words in speech processing tasks. we introduce a simple, unsupervised method that outperforms the conventional supervised method of forced alignment with a reference. the success of this method is independently demonstrated using three metrics from large-scale speech tasks: word error rates for large vocabulary continuous speech recognition, decision error tradeoff curves for spoken term detection, and phone error rates compared to a handcrafted pronunciation lexicon. the experiments were conducted using state-of-the-art recognition, indexing, and retrieval systems. the results were compared across many terms, hundreds of hours of speech, and well known data sets.
sound localisation with a silicon cochlea pair. a neuromorphic sound localisation system is proposed. it employs two microphones and a pair of silicon cochleae with address event interface for front-end processing. this allows subsequent processing to be implemented with spike-based algorithms. the system is adaptive and supports online learning. its localisation capability was tested with white noise and pure tone stimuli, with an average error of around 3° in the −45° to 45° range.
investigating glottal parameters for differentiating emotional categories with similar prosodics. speech prosodics (i.e., pitch, energy, etc.) play an important role in the interpretation of emotional expression. however, certain pairs of emotions can be difficult to discriminate due to similar displayed tendencies in prosodic statistics. the purpose of this paper is to target speaker dependent expressions of emotional pairs that share statistically similar prosodic information and investigate a set of glottal features for their ability to find measurable differences in these expressions. evaluation is based on acted emotional utterances from the emotional prosody and speech transcript (epst) database. while it is in no way assumed that acted speech provides a complete picture of authentic emotion, the value of this information is that the actors adjusted their voice quality to fit their perception of different emotions. results show statistically significant differences (p ≪ 0.05) in at least one glottal feature for all 30 emotion pairs where prosodic features did not show a significant difference. in addition, the use of single glottal features reduced classification error for 24 emotion pairs in comparison to pitch or energy.
small-group learning projects to make signal processing more appealing: from speech processing to ofdma synchronization. whereas lecturing is the most widely used mode of instruction, we have explored small-group learning projects to make signal processing more appealing at the university and in engineering schools in bordeaux (france). the projects cover a wide range of applications, from audio processing to mobile communication system analysis and can be based on problems suggested by industrial partners. after a state of the art in the area, the students develop signal processing algorithms and usually deliver new softwares. at the end of the semester, they provide a final 8-page report and present their work during a one-day workshop. the projects enable the students to experience cooperative works, which is mostly done in industry. in addition, they help the students to see more easily the links between the courses they follow, including project managing. the students are hence involved in dialog inside their own group, in writing english reports as well as in problem solving, analysis and synthesis. this paper presents four examples dealing with speech processing, spectral analysis, universal mobile telecommunications system (umts) mobile positioning and orthogonal frequency division multiple access (ofdma) synchronization.
progressive distributed estimation over noisy channels in wireless sensor networks. this paper presents a new progressive distributed estimation scheme (des) along with the power scheduling among sensors under awgn channels. the progressive des consists of a transmission bit allocation scheme and a quasi best linear unbiased estimate (blue) of the unknown parameter at each sensor. this scheme is shown to outperform the traditional progressive des. moreover, the power scheduling among sensors, which minimizes the total transmission power subject to the desired mse performance tolerance, is formulated as a convex problem and the optimal solution is derived analytically.
a new method for speaker adaptation using bilinear model. in this paper, a novel method for speaker adaptation using bilinear model is proposed. bilinear model can express both characteristics of speakers (style) and phonemes across speakers (content) independently in a training database. the mapping from each speaker and phoneme space to observation space is carried out using bilinear mapping matrix which is independent of speaker and phoneme space. we apply the bilinear model to speaker adaption. using adaptation data from a new speaker, speaker-adapted model is built by estimating the style(speaker)-specific matrix. experimental results showed that the proposed method outperformed eigenvoice and mllr. in vocabulary-independent isolated word recognition for speaker adaptation, bilinear model reduced word error rate by about 38% and about 10% compared to eigenvoice and mllr respectively using 50 words for adaptation.
transcription and expressiveness detection system for violin music. in this paper, a transcription system for music played by violin is presented. the transcription system not only detects the pitch and duration of the notes but also identifies successfully the employed technique to play each note: détaché with and without accent and with and without vibrato, pizzicato, tremolo, spiccato and flageolett-töne. the transcription system is based on a combined analysis of the time domain and frequency domain properties of the music signal.
speaker recognition using syllable-based constraints for cepstral frame selection. we describe a new gmm-ubm speaker recognition system that uses standard cepstral features, but selects different frames of speech for different subsystems. subsystems, or “constraints”, are based on syllable-level information and combined at the score level. results on both the nist 2006 and 2008 test data sets for the english telephone train and test condition reveal that a set of eight constraints performs extremely well, resulting in better performance than other commonly-used cepstral models. given the still largely-unexplored world of possible constraints and combinations, it is likely that the approach can be even further improved.
multiple-peak model fitting function for dct sign phase correlation with non-integer shift precision. we propose two fitting functions for shift estimation using discrete cosine transform sign phase correlation (dct-spc) with non-integer accuracy. the dct-spc can be used in order to estimate the shift values and the similarity between two signals. however, the estimated shift values are limited to integer numbers. the proposed fitting functions enable the dct-spc to estimate the non-integer shift values using only the sign of the dct coefficients. the multiple-peak model in the proposed fitting functions provides more accurate values than other models. simulations are presented to demonstrate the effectiveness of the proposed fitting functions.
copulas based multivariate gamma modeling for texture classification. this paper deals with texture modeling for classification or retrieval systems using multivariate statistical features. the proposed features are defined by the hyperparameters of a copula-based multivariate distribution characterizing the coefficients provided by image decomposition in scale and orientation. as it belongs to the multivariate stochastic models, the copulas are useful to describe pairwise non-linear association in the case of multivariate non-gaussian density. in this paper, we propose the d-variate gaussian copula associated to univariate gamma densities for modeling the texture. experiments were conducted on the vistex database aiming to compare the recognition rates of the proposed model with the univariate generalized gaussian model, the univariate gamma model, and the generalized gaussian copula-based multivariate model.
data pruning-based compression using high order edge-directed interpolation. this paper proposes a data pruning-based compression scheme to improve the rate-distortion relation of compressed images and video sequences. the original frames are pruned to a smaller size before compression. after decoding, they are interpolated to their original size by an edge-directed interpolation. the data pruning is optimized to obtain the minimal distortion in the interpolation phase. furthermore, a novel high order interpolation is proposed to adapt the interpolation to many edge directions. this high order filtering uses extra surrounding pixels and achieves more robust edge-directed image interpolation. simulation results are shown for both image interpolation and coding applications.
head-mounted active noise control system for mr noise. recently, magnetic resonance imaging (mri) devices are used in many medical institutions on the grounds of safety and convenience. an open-configuration mr system is introduced at shiga university of medical science in order to conduct microwave coagulation therapy by using near-real-time mr images. however, this system has a fatal defect. when mri device works to take images, it also generates serious noises (mr noise). hence, an operator and other medical staff (ex. nurses and anesthetists) suffer from mr noise and cannot communicate with each other during the operation. in this paper, we therefore propose a head-mounted anc system in order to reduce the mr noise, and some experimental results demonstrate cancellation performance of the system implemented by digital signal processor (dsp).
the i4u system in nist 2008 speaker recognition evaluation. this paper describes the performance of the i4u speaker recognition system in the nist 2008 speaker recognition evaluation. the system consists of seven subsystems, each with different cepstral features and classifiers. we describe the i4u primary system and report on its core test results as they were submitted, which were among the best-performing submissions. the i4u effort was led by the institute for infocomm research, singapore (iir), with contributions from the university of science and technology of china (ustc), the university of new south wales, australia (unsw), nanyang technological university, singapore (ntu) and carnegie mellon university, usa (cmu).
maximizing the continuity in segmentation - a new approach to model, segment and recognize speech. this paper presents a new approach to speech modeling and recognition. the new approach consists of a statistical model to represent up to sentence-long temporal dynamics in the training data, and an algorithm to identify the matching segments with maximum continuities between the training and testing sentences. recognition is performed by combining the longest matching segments found from the training sentences. because of their richer and more distinct temporal dynamics, longer speech segments as whole units can be recognized with lower error rates than shorter speech segments. therefore basing recognition on the longest matching segments optimizes the discrimination and hence recognition of speech. the new approach has been evaluated on the timit database for identifying matching speech segments. the results obtained are encouraging given the very low parametric complexity of the new model. 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-channel audio segmentation for continuous observation and archival of large spaces. in most real-world situations, a single microphone is insufficient for the characterization of an entire auditory scene. this often occurs in places such as office environments which consist of several interconnected spaces that are at least partially acoustically isolated from one another. to this end, we extend our previous work on segmentation of natural sounds to perform scene characterization using a sparse array of microphones, strategically placed to ensure that all parts of the environment are within range of at least one microphone. by accounting for which microphones are active for a given sound event, we perform a multi-channel segmentation that captures sound events occurring in any part of the space. the segmentation is inferred from a custom dynamic bayesian network (dbn) that models how event boundaries influence changes in audio features. example recordings illustrate the utility of our approach in a noisy office environment.
multiple time resolution analysis of speech signal using mce training with application to speech recognition. in this paper, we propose two methods of multiple time-resolution analysis of speech and their application to automatic speech recognition (asr). constant frame-rate multi-scale analysis is proposed based on a box of multi-scale features. then a variable rate analysis is proposed based on the selection of the optimal temporal resolution on the fly by a properly trained non-linear classifier unit. the classifier's parameters are trained using the discriminative method of minimum classification error (mce) training. we use the recently proposed conditional random fields (crf) phonetic recognition system that effectively combines highly correlated features. results are reported on a frame-wise classification task and also on timit phone recognition task. results show that (i) crfs can effectively combine multi-scale features and (ii) mce trained variable rate crfs are competitive with the “box” combination method.
modeling and tracking of faces in real-life illumination conditions. in this paper, we address the problem of face tracking across illumination changes and occlusions. the method is based on leveraging the strengths of both adaboost to deal with clutter and the image based parametric illumination model proposed by kale and jaynes. we show that a simple non-linear transformation of the adaboost score multiplied with the illumination compensated likelihood leads to a fast robust tracking paradigm. we demonstrate the ability of our method to detect occlusions at the same time ensuring that mis-assignments between the occluder and the occluded does not occur. we present experimental results of our method on low resolution surveillance indoor and outdoor videos using an off the shelf dsp. we also demonstrate the power of the parametric illumination model for pose constrained face recognition when matching across known illumination conditions.
phase unwrapping for interferometric sar using multibaseline joint data group. phase unwrapping is the key problem in building the digital elevation model (dem) of a scene from interferometric synthetic aperture radar (sar) system data. in this paper, we propose a method of phase unwrapping based on the model of the multibaseline joint data group. the method can not only adaptively coregister the sar images, but also accurately provide the accurate estimation of the terrain unwrapped phase in the presence of the large coregistration errors. moreover, the improvement in computational complexity is achieved by using the multibaseline joint data group. the method is investigated by simulations, and results show successful phase unwrapping even if the image coregistration error is close to one pixel.
the dsp of money. this paper discusses a simple framework that can be used to connect a significant number of the tools and techniques developed in a first course in either discrete-time signals and systems or digital signal processing. while this framework is not revolutionary, it allows for the rapid placement of new material into the course's context. additionally, a simple model of an unstable financial system allows for a rapid introduction and overview of the course while observably increasing student interest and motivation. in these challenging economic times, it is somewhat reassuring to know that some unstable financial accounts are actually a good thing.
cubic higher-order criterion and algorithm for blind extraction of a source signal. the paper deals with the problem of the blind extraction of a source signal after a mimo convolutive mixture. the extraction is performed using a miso equalizer. a contrast function based on high order statistics is first proposed. it is more general than the existing contrast in the same context and exhibits a cubic dependence w.r.t. the unknown equalizer parameters. this allows us to propose a new extraction algorithm based on a third order tensor decomposition. computer simulations illustrate the good behavior and the usefulness of our algorithm.
a custom-designed mental task-based brain-computer interface. at present, brain-computer interfaces cannot be used in real-life applications mainly because of their high false activation rates. to achieve a zero false positive rate, a mental task-based brain-computer interface custom designed for each subject and each task is proposed. the most discriminatory mental task is determined for each subject. we used the eeg signals of four subjects recorded while they were performing five different mental tasks. autoregressive modeling and stationary wavelet transform are used in the process of feature extraction. classification is based on quadratic discriminant analysis. for the most discriminatory mental task of each subject, we achieved a false positive rate of zero value while the true positive rate obtained was above 60%.
hands-free speech recognition challenge for real-world speech dialogue systems. in this paper, we describe and review our recent development of hands-free speech dialogue system which is used for railway station guidance. in the application at the real railway station, robustness against reverberation and noise is the most essential issue for the dialogue system. to address the problem, we introduce two key techniques in our proposed hands-free system; (a) speech dialogue system construction with real speech database collection and language/acoustic model improvement, and (b) microphone array preprocessing using blind spatial subtraction array which can solve the reverberation-naiveness problem inherent in conventional microphone arrays. the experimental assessment of the proposed dialogue system reveals that our system can provide the recognition accuracy of more than 80% under realistic railway-station conditions.
experimental study of extended target imaging by time reversal sar. conventional sar images under rich scattering suffer degradation because of ghost images caused by multipath propagation. in this paper, we develop a time reversal sar (tr-sar) imaging algorithm for extended (nonpoint-like) targets in rich multipath scattering. we test the tr-sar algorithm using experimental electromagnetic data collected in a laboratory environment where the extended target (a galvanized steel sheet) is surrounded by a large amount of pvc rods. our experiments show that the collected em data in frequency and aperture after tr-sar processing produces a higher resolution, cleaner target map compared with conventional sar images.