waashk/dblp huggingface dataset:
robust speech recognition based on structured modeling, irrelevant variability normalization and unsupervised online adaptation. we present a new approach to robust speech recognition based on structured modeling, irrelevant variability normalization (ivn) and unsupervised online adaptation (ola). in offline training stage, a set of generic hmms for basic speech units relevant to phonetic classification is trained along with several sets of feature transforms with different degrees of freedom by using a maximum likelihood (ml) ivn-based training strategy. in recognition stage, after a first-pass recognition, the most appropriate set of feature transforms is identified and adapted under ml criterion by using the unknown utterance itself, which is recognized again to achieve better performance by using the adapted feature transforms and the pre-trained generic hmms. the effectiveness of the proposed approach is confirmed by evaluation experiments on finnish aurora3 database.
learning in diffusion networks with an adaptive projected subgradient method. we present an algorithm that minimizes asymptotically a sequence of non-negative convex functions over diffusion networks. to account for possible node failures, position changes, and/or reachability problems (because of moving obstacles, jammers, etc), the algorithm can cope with dynamic networks and cost functions, a desirable feature for online algorithms where information arrives sequentially. many projection-based algorithms can be straightforwardly extended to diffusion networks with the proposed scheme. we use the acoustic source localization problem in sensor networks as an example of a possible application.
a harmonic bandwidth extension method for audio codecs. today's efficient audio codecs for low bitrate application scenarios often rely on parametric coding of the upper frequency band portion of a signal while the lower frequency band portion of the same is conveyed by a waveform preserving coding method. at the decoder, the upper frequency signal is approximated from the lower frequency data using the upper frequency band parameters. however, commonly used methods of bandwidth extension almost inevitably suffer from a sensation of unpleasant roughness, which is especially present for tonal music items. in this paper we expose the origin of the roughness and propose a bandwidth extension method, which does not introduce roughness into the reconstructed audio signal. a listening test demonstrates the advantage of the proposed method compared to a standard bandwidth extension.
unsupervisec cross-validation adaptation algorithms for improved adaptation performance. an unsupervised cross-validation adaptation algorithm and its variation are proposed that introduce the idea of cross-validation in the unsupervised batch-mode adaptation framework to improve the adaptation performance. the first algorithm is constructed on a general adaptation technique such as mllr and can be used in combination with any adaptation method. the second algorithm is a modified version of the first algorithm and works with lower computational cost by assuming mllr. these algorithms are extensions of our previously proposed cv training methods and are useful to suppress the negative effect of the conventional unsupervised batch-mode adaptation process that reinforces the errors included in automatic transcriptions. the proposed algorithms were evaluated in domain adaptation, speaker adaptation, and in their combination for large vocabulary spontaneous speech recognition. when the domain and speaker adaptations were combined using a read speech initial model, the relative word error rate reduction by the proposed method was 29% whereas the reduction by the conventional approach was 23%.
robust target localization in moving radar platform through semidefinite relaxation. accurate target localization is an important task in various commercial and military applications. one way to achieve this goal is to use the time-of-arrival (toa) or time-delay-of-arrival (tdoa) information observed at multiple distributed sensors. on the other hand, there is a great need to use moving sensors to form a radar platform with synthetic apertures. in this paper, we consider the problem of target localization based on the range information estimated from two-way time-of-flight (tw-tof) at multiple synthetic array locations, where the position of these synthetic array locations is subject to certain random errors. the nonconvex estimation problem is approximated by a convex optimization problem using the semidefinite relaxation (sdr) approach. simulation results show that the proposed estimator provides mean square position error performance close to the cramer-rao lower bound.
low-delay scheduling for grassmannian beamforming with a sinr constraint. we are presenting an algorithm for scheduling users in a single-cell broadcast scenario. the presented algorithm aims to minimize the number of transmissions that are necessary to serve all the users in the cell a single time, while the different users still fulfill a strict sinr constraint. depending on the individual channel characteristics, the algorithm adapts the number of users scheduled for transmission on the fly, and dynamically allocates the transmit power to the scheduled users. a high-performance and a low-complexity variant of the algorithm are presented and their performance is evaluated through simulations.
direction of arrival estimation using single tripole radio antenna. we consider the problem of estimating the direction of arrival (doa) of multiple waves incident on a single tripole sensor. using the physical properties of the electric and magnetic fields, we show that we can disambiguate the doa of multiple simultaneously incident waves using a set of time sampled 3d measurements from the single sensor. this is different from the traditional approach that uses arrays of antennas to estimate doa. we use the unitary matrix pencil method to estimate the frequencies of the waves, and use a least squares solver to estimate the amplitude and phase coefficients. we combine these to compute the doa and evaluate the approach using simulations. we show that the method is very effective at estimating doa for different numbers of incident waves, and different noise levels.
applications of complex augmented kernels to wind profile prediction. this paper combines complex signal processing with kernel methods for applications in wind prediction. specifically, we consider developing least squares kernel algorithms for both complex data and augmented complex data. the augmented complex kernel algorithms have advantages over complex kernel algorithms in both the areas of performance and complexity. use of kernels also allow implementation of nonlinear algorithms by working in the dual space. we apply our algorithm to wind series time prediction and show that our augmented complex algorithms outperform other complex least square algorithms.
improved 3d assisted pose-invariant face recognition. recent face recognition algorithm can achieve high accuracy when testing face samples are frontal. however, when face pose changes largely, the performance of existing methods drop drastically. in this paper, we propose an improved algorithm aiming at recognizing faces of different poses when each face class has only one frontal training sample. for each sample, a 3d face is constructed by using 3d morphable model (3dmm). the shape and texture parameters of 3dmm are recovering by fitting the model to the 2d face sample which is a non-linear optimization problem. the virtual faces of different views are generated from the 3dmm to assist face recognition. different from the conventional optimization energy function, proposed energy function takes not only image intensity but also shape constraint into account. in this paper, we locate 88 sparse points from the 2d face sample by automatic face fitting and use their correspondence in the 3d face as shape constraint. we experiment proposed method on the publicly available cmupie database which includes faces viewed from 11 different poses and the results show that proposed method is effective and the face recognition results towards pose-variant are promising.
multiuser transmit beamforming for mimo uplink with individual sinr targets. for multiuser multiple-input multiple-output (mimo) uplink communications where the signal-to-interference-plus-noise ratio (sinr) of the k-th user, denoted as sinrk, is expected to achieve the target value γk, we first consider the ideal scenario where multiuser channel matrices are perfectly known to every user and we solve the problem of maximizing mink(sinrk/γk) over all possible beamformers under individual transmit power constraints via alternating optimization strategies and the generalized eigenvalue problem (gevp) programming. then, we study a more realistic situation where only a few number of bits are allowed to feed back to users. we propose to integrate the genetic algorithm into the lloyd's vector quantization method to design the optimum codebook in terms of maximizing e(mink(sinrk/γk)) with expectation taken with respect to fading channels.
music emotion ranking. content-based retrieval has emerged as a promising approach to information access. in this paper, we propose an approach to music emotion ranking. specifically, we rank music in terms of arousal and valence and represent each song as a point in the 2d emotion space. novel ranking-based methods for annotation, learning, and evaluation of music emotion recognition are developed and tested on a moderately large-scale database composed of 1240 pop songs. results are provided to show the feasibility of the proposed approach.
analog compressed sensing. a traditional assumption underlying most data converters is that the signal should be sampled at a rate exceeding twice the highest frequency. practical signals often posses a sparse structure so that a large part of the bandwidth is not exploited. in this paper, we consider a framework for utilizing this sparsity in order to sample such analog signals at a low rate. by relying on results developed in the context of compressed sensing (cs) of finite-length vectors, we develop a general framework for low-rate sampling of signals in shift-invariant spaces. in contrast to the problems treated in the context of cs, here we explicitly consider sampling of analog signals for which no underlying finite-dimensional model exists.
biometric recognition by fusing palmprint and hand-geometry based on morphology. this paper presents a novel biometric recognition system fusing the palmprint and hand geometry of a human hand based on morphology. we utilize the image morphology and concept of voronoi diagram to cut the image of the front of the whole palm apart into several irregular blocks in accordance with the hand geometry. furthermore, statistic characteristics of the gray level in the blocks are employed as characteristic values. the experimental results show that the proposed system has an encouraging performance. the false acceptance rate (far) and false rejection rate (frr) are reduced down to 0.0035% and 5.7692%, respectively.
video coding using variable block-size spatially varying transforms. in our previous work, we introduced spatially varying transforms (svt) for video coding, where the location of the transform block within the macroblock is not fixed but varying. in this paper, we extend this concept and present a novel method, called variable block-size spatially varying transforms (vbsvt). vbsvt utilizes variable block-size transforms (vbt) in the svt framework, and is shown to be more preferable for coding prediction error with different characteristics than fixed block-size svt and also the standard methods that use fixed or adaptive block sizes at fixed spatial locations. in addition, vbsvt has similar decoding complexity with fixed block-size svt and lower decoding complexity compared to standard methods as only a portion of the prediction error needs to be decoded. experimental results show that, vbsvt achieves 4.1% gain over h.264/avc on average over a wide range of test set. gains become more significant at high quality levels and go up to 13.5%, which makes the proposed algorithm very suitable for future video coding solutions focusing on high fidelity applications.
audiovisual celebrity recognition in unconstrained web videos. the number of video clips available online is growing at a tremendous pace. conventionally, user-supplied metadata text, such as the title of the video and a set of keywords, has been the only source of indexing information for user-uploaded videos. automated extraction of video content for unconstrained and large scale video databases is a challenging and yet unsolved problem. in this paper, we present an audiovisual celebrity recognition system towards automatic tagging of unconstrained web videos. prior work on audiovisual person recognition relied on the fact that the person in the video is speaking and the features extracted from audio and visual domain are associated with each other throughout the video. however, this assumption is not valid on unconstrained web videos. proposed method finds the audiovisual mapping and hence improve upon the association assumption. considering the scale of the application, all pieces of the system are trained automatically without any human supervision. we present the results on 26,000 videos and show the effectiveness of the method per-celebrity basis.
interpolatory mercer kernel construction for kernel direct lda on face recognition. this paper proposes a novel methodology on mercer kernel construction using interpolatory strategy. based on a given symmetric and positive semi-definite matrix (gram matrix) and cholesky decomposition, it first constructs a nonlinear mapping φ, which is well-defined on the training data. this mapping is then extended to the whole input feature space by utilizing lagrange interpolatory basis functions. the kernel function constructed by inner product is proven to be a mercer kernel function. the self-constructed interpolatory mercer (im) kernel keeps the gram matrix unchanged on the training samples. to evaluate the performance of the proposed im kernel, a popular kernel direct linear discriminant analysis (kdda) method for face recognition is selected. comparing with rbf kernel based kdda method on two face databases, namely feret and cmu pie databases, the im kernel based kdda approach could increase the performance by around 20% on cmu pie database.
speech reinforcement based on partial masking effect. perceived quality of the speech signal deteriorates significantly in the presence of ambient noise. in this paper, based on the analysis that the partial masking effect is a main source of the quality degradation when interfering signals are present, we propose a novel approach to enhance the perceived quality of speech signal when the ambient noise cannot be directly controlled by reinforcing it so that it can be heard more clearly. to find a suitable reinforcement rule, the loudness perception model proposed by moore et al. [1] is adopted with the consideration on the prevention of the hearing damage. experimental results show that the perceived quality and intelligibility can be enhanced under various noise environments.
tensor-based channel estimation (tence) for two-way relaying with multiple antennas and spatial reuse. in this paper we study two-way relaying with amplify-and-forward (af) relays. in two-way relaying, two terminals exchange data with the help of an intermediate relay station. in order to enable mass deployment of these relays, we focus on very simple af relays that do not have any channel state information. hence, to separate the data streams in two-way relaying, both user terminals need reliable knowledge of all relevant channel parameters. we therefore propose the novel tensor-based channel estimation algorithm tence that provides both terminals with full knowledge of all channel parameters involved in the transmission. the solution is algebraic, i.e., it does not require any iterative procedures. moreover, tence is applicable to arbitrary antenna configurations. we also derive criteria for the design of the pilot symbols and the corresponding relay amplification matrices. computer simulations demonstrate the achievable channel estimation accuracy.
minimum ber beamforming in the rf domain for ofdm transmissions and linear receivers. in this paper, we study transmission schemes for a novel ofdm-based mimo system which performs adaptive signal combining in radio-frequency (rf). specifically, we consider the problem of selecting the linear precoder and the transmit and receive rf weights (or beamformers) for minimizing the bit error rate (ber) under the assumption of perfect channel knowledge and linear receivers. firstly, it is shown that the optimal precoder amounts to uniformly distribute the overall mean square error (mse) among the information symbols. secondly, we propose a gradient search algorithm to obtain the optimal pair of beamformers. interestingly, in the case of low signal to noise ratios (snr), the proposed beamforming criterion is equivalent to the maximization of the received snr. however, for moderate and high snrs, part of the received snr is sacrificed in order to improve the channel response of the worst subcarriers, which translates into significant advantages over other previously proposed approaches. finally, the performance of the proposed scheme is illustrated by means of some numerical examples.
robust localization in sensor networkswith iterative majorization techniques. self localization in sensor networks with measurements that include outliers is an important problem. e.g., distance measurements based on non-line-of-sight observations can be quite wrong. if not handled properly, such outliers can greatly influence the positioning accuracy. to achieve robustness we consider positioning with huber estimators. the huber cost function interpolates between the ℓ1 and the ℓ2 norms. the minimization of the huber cost function can be efficiently obtained via iterative majorization techniques, with the advantageous property of guaranteed convergence to a local minimum.
widely-linear recursive least-squares algorithm for adaptive beamforming. adaptive beamforming algorithms typically rely on a complex linear model between the sensor measurements and the desired signal output that does not enable the best performance from the data in some situations. in this paper, we present an extension of the well-known recursive least-squares algorithm for adaptive filters to widely-linear complex-valued signal and system modeling. the widely-linear rls algorithm exploits a structured covariance matrix update that maintains information about the non-circularity of the input data to solve the widely-linear least-squares task at each snapshot. in addition, the wl-rls algorithm can easily be switched between conventional and widely-linear complex modeling as needed. application of the method to adaptive beamforming of mixed bpsk and qpsk signal transmissions shows that the system can extract all of the transmitted signal outputs in certain overloaded scenarios, and it performs up to 3db better than the conventional rls beamformer when the array is not overloaded.
on combinations of cma equalizers. we extend the affine combination of one fast and one slow least mean-square (lms) filter to blind equalization, considering the combination of two constant modulus algorithms (cma). we analyze the proposed combination in stationary and nonstationary environments verifying that there are situations where the absence of the restriction on the mixing parameter can be advantageous for the combination. furthermore, we propose a combination of two cmas with different initializations. preliminary simulations show that this scheme can avoid local minima and eventually can present a faster convergence rate than that of its components.
low-complexity bandwidth extension in mdct domain for low-bitrate speech coding. we propose a low-complexity bandwidth extension (bwe) method operating in the modified discrete cosine transform (mdct) domain to reduce the bitrate of wideband and super-wideband speech codecs. the proposed method generates a high-frequency signal by copying the mdct spectrum from the low frequency part, and then adjusts tonality to improve the subjective quality of the generated high-frequency signal. in combination with an mdct-based transform codec, it requires only 64.9% of the computational complexity of mpeg-4 spectral band replication (sbr). it also achieves subjective quality better than sbr for many speech samples.
mobhrg: fast k-nearest-neighbor search by overlap reduction of hyperspherical regions. we propose a minimum overlap based hyperspherical region graph indexing structure to achieve fast similarity-based queries for both low and high dimensional datasets. specifically, we reduce the region overlaps in the graph construction phase by incrementally dividing each saturated hyperspherical region and removing the longest edge of a minimum spanning tree representation of the internal objects. this overlap reduction scheme creates more separated regions, so fewer regions as potential paths are traversed when a query is issued. we also introduce a k-nearest-neighbor search scheme by automatically deciding the search radius to return the required number of nearest neighbors. our extensive experimental results show the effectiveness of the proposed indexing structure compared with other tree and graph based indexing structures.
comparative evaluation of pedestrian detection methods for mobile bus surveillance. we present a comparative evaluation of the state-of-art algorithms for detecting pedestrians in low frame rate and low resolution footage acquired by mobile sensors. four approaches are compared: a) the histogram of oriented gradient (hog) approach [1]; b) a new histogram feature that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated gaussian filters [2] corresponding to the quantised orientation, called histogram of oriented gradient banks (hogb) approach; c) the codebook based hog feature with branch-and-bound (efficient subwindow search) algorithm [3] and; d) the codebook based hogb approach. results show that the hog based detector achieves the highest performance in terms of the true positive detection, the hogb approach has the lowest false positives whilst maintaining a comparable true positive rate to the hog, and the codebook approaches allow computationally efficient detection.
blind adaptive beamforming for wideband circular arrays. an approach for blind adaptive wideband beamforming is proposed based on a uniform circular array. the received array signals are first transformed into different phase modes and each phase mode output is then processed by a filter to achieve a frequency independent response. as a result, a set of instantaneous mixtures of the original source signals is obtained and the original wideband beamforming problem can be readily solved using the standard instantaneous bss algorithms and the original source signals can be recovered one by one or simultaneously, depending on the specific requirement.
bayesian pursuit algorithm for sparse representation. in this paper, we propose a bayesian pursuit algorithm for sparse representation. it uses both the simplicity of the pursuit algorithms and optimal bayesian framework to determine active atoms in sparse representation of a signal. we show that using bayesian hypothesis testing to determine the active atoms from the correlations leads to an efficient activity measure. simulation results show that our suggested algorithm has better performance among the algorithms which have been implemented in our simulations in most of the cases.