waashk/dblp huggingface dataset:
text data acquisition for domain-specific language models. the language modeling community is showing a growing interest in using large collections of text mined from the world wide web (www) to supplement sparse in-domain text resources. however, in most cases the style and content of the text harvested from these corpora differs significantly from the specific nature of these domains. in this paper we present a relative entropy (r.e.) based method to select relevant subsets of sentences whose distribution in an n-gram sense matches the domain of interest. using simulations, we provide an analysis of how the proposed scheme outperforms filtering techniques proposed in recent language modeling literature on mining text from the web. a comparative study is presented using a text collection of over 800m words collected from the www. experimental results show that by using the proposed subset selection scheme we can get performance improvement in both word error rate (wer) and perplexity (ppl) over the models built from the entire collection by using just 10% of the data. improvements in data selection also translated to a significant reduction in the vocabulary size as well as the number of estimated parameters in the adapted language model.
restoring punctuation and capitalization in transcribed speech. adding punctuation and capitalization greatly improves the readability of automatic speech transcripts. we discuss an approach for performing both tasks in a single pass using a purely text-based n-gram language model. we study the effect on performance of varying the n-gram order (from n = 3 to n = 6) and the amount of training data (from 58 million to 55 billion tokens). our results show that using larger training data sets consistently improves performance, while increasing the n-gram order does not help nearly as much.
joint source/relay precoders design in amplify-and-forward relay systems: a geometric mean decomposition approach. existing precoder designs for an amplify-and-forward (af) cooperative system often assume a linear receiver at the destination, and a precoder at the relay. the performance enhancement of such a system is then limited. in this paper, we consider a nonlinear successive interference cancellation (sic) receiver, and at the same time take the source precoder into consideration. using the geometric mean decomposition (gmd), we propose a joint source/relay precoders design method, fully exploring information provided by direct and relay links. with our method, the design problem can be transformed to a standard scalar concave optimization problem, and a closed-form solution can be obtained. simulations show that the proposed design can significantly enhance the performance of a mimo af cooperative system.
full covariance state duration modeling for hmm-based speech synthesis. this paper proposes a state duration modeling method using full covariance matrix for hmm-based speech synthesis. in this method, a full covariance matrix instead of the conventional diagonal covariance matrix is adopted in the multi-dimensional gaussian distribution to model the state duration of each context-dependent phoneme. at synthesis stage, the state durations are predicted using the clustered context-dependent distributions with full covariance matrices. experimental results show that the synthesized speech using full-covariance state duration models is more natural than the conventional method when we change the speaking rate of synthesized speech.
what happens when cognitive terminals compete for a relaying node? we introduce a new channel, which consists of an interference channel (ic) in parallel with an interference relay channel (irc), to analyze the interaction between two selfish and cognitive transmitters who compete for a relay implementing the amplify-and-forward protocol. it is shown that whatever the relay location there is always an equilibrium in the resource allocation game where the users selfishly share their power between the ic and irc. the uniqueness and determination of this equilibrium is analyzed for two cases: the relay amplification gain is fixed; the irc direct links are negligible. we show how to exploit this analysis to optimally locate the relay either in terms of individual rate or system sum-rate. simulations are provided and show, in particular, how the users' selfish behavior leads to sharing the space in regions where the relay is used by only one user or not used at all.
analog flat filter design. this paper proposes a systematic approach for the design of a general class of analog infinite-impulse-response (iir) filters, which includes all well-known classical analog filters as a special case. all specifications including the conventional ones and also filter flatness degrees are explicitly incorporated into design process. several numerical examples are presented to demonstrate the efficiency and flexibility of the proposed method.
vector perturbation precoding for receivers with limited dynamic range. in this paper we consider the vector perturbation (vp) precoding scheme for the multiuser miso broadcast channel proposed by hochwald et al. under the practical assumption that the receivers have limited dynamic range. in this case, vp precoding is shown to suffer from an error floor at high signal-to-noise ratio (snr). as an alternative, we propose precoding with restricted vp (rvp), which takes the limited dynamic range of the receivers explicitly into account by restricting to a finite set of possible perturbation vectors at the transmitter side. we derive the diversity order of this rvp scheme and show that no error floor occurs and that the performance is superior to vp for the entire range of snrs.
rate-constrained distributed distance testing and its applications. we investigate a practical approach to solving one instantiation of a distributed hypothesis testing problem under severe rate constraints that shows up in a wide variety of applications such as camera calibration, biometric authentication and video hashing: given two distributed continuous-valued random sources, determine if they satisfy a certain euclidean distance criterion. we show a way to convert the problem from continuous-valued to binary-valued using binarized random projections and obtain rate savings by applying a linear syndrome code. in finding visual correspondences, our approach uses just 49% of the rate of scalar quantization to achieve the same level of retrieval performance. to perform video hashing, our approach requires only a hash rate of 0.0142 bpp to identify corresponding groups of pictures correctly.
compressed sensing and multistatic sar. we demonstrate that the remarkable advantages of compressed sensing remain in force when the information operator is constrained to obey the physical rules of a multistatic sar measurement. the design guidelines of the sar information operator for ℓ2 reconstructions is compared to those provided for generic ℓ1 reconstructions. we report little or no degradation in compression performance when using an information operator obeying sar sampling constraints. simulations for a shepp-logan image show an image is faithfully reconstructed when the number of measurements is about a third of the number of image pixels, using a minimum total-variation technique. we observed high sensitivity in performance and algorithm convergence to small perturbations in the measurement vectors.
distributed beamforming for wireless sensor networks with random node location. due to the unsupervised nature of wireless sensor networks (wsns), intensive communications are required among the selected nodes to reach a consensus and synchronize prior to entering a distributed beamforming (dbf) procedure. therefore, a sensible approach to select the nodes should not only take into account the required beampattern, but also should aim to preserve the inter-node connectivity and the network energy. we show for a uniformly distributed wsn that when the nodes are selected from a ring of proper radii, the resulting beampattern mainlobe is narrower compared to that of the classical dbf technique proposed in [1]. at the same time, our proposed technique may preserve a substantial amount of network energy and reduce the probability of network disconnectivity. directivity of the proposed dbf technique is analyzed and an extension of the technique to a multi-ring case is presented. it is shown that the sidelobe peaks can be considerably decreased if the nodes are selected from multiple concentric rings.
a weighted logarithmic merit function for canonical correlation analysis. a weighted logarithmic merit function that incorporates a diagonal matrix is utilized for deriving a gradient dynamical system that converges to the actual canonical correlation coordinates of arbitrary data matrices. the equilibrium points of the resulting gradient system are determined and their stability is thoroughly analyzed. qualitative properties of the proposed systems are analyzed in detail including the limit of solutions as time approaches infinity. the performance of this system is also examined. 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
new discrete inverse s transform with least square error in filtering. the s transform is a useful linear time-frequency distribution and has been applied to various fields. since the inverse s transform is an over-determined problem, there exist several different algorithms, which result in different filtering effects. this paper models the discrete s transform in matrix form and proposes a new inverse s transform algorithm with least square error in filters. this paper also compares the new inverse s transform with the previous ones.
low bitrate audio coding using generalized adaptive gain shape vector quantization across channels. audio coding at low bitrates suffers from artifacts due to spectrum truncation. typical audio codecs code multi-channel sources using transforms across the channels to remove redundancy such as middle (mid) - side (m/s) coding. at low bitrates, the spectrum of the coded channels is truncated and the spectrum of the channels with lower energy, such as the side channel, is truncated severely, sometimes entirely. this results in a muffled sound due to truncation of all coded channels beyond a certain frequency. it also results in a loss of spatial image even at low frequencies due to severe truncation of the side channel. previously we have developed a low bitrate coding method to combat the loss of higher frequencies caused by spectrum truncation. in this paper, we present a novel low bitrate audio coding scheme to mitigate the loss of spatial image. listening tests show that the combination of the two low bitrate coding methods results in a audio codec that can get good quality even at bitrates as low as 32kbps for stereo content with low decoder complexity.
optimum multi-target detection using an anc neuro-fuzzy scheme and wideband replica correlator. an optimum target detection algorithm is developed for underwater active wideband sonar signal in the presence of reverberation and ambient noise. the hybrid algorithm makes use of an adaptive anc neuro-fuzzy scheme in the first instance to provide an effective fine tuned signal followed by an iterative cross correlation-based target motion estimation (tme) scheme for efficient target detection. computer simulations based on real input data sets demonstrate capability and efficiency of the proposed hybrid algorithm in extracting even zero-doppler target signals from a highly reverberated noisy environment.
eeg signal classification using nonlinear independent component analysis. one of the preprocessors can be used to improve the performance of brain-computer interface (bci) systems is independent component analysis (ica). ica is a signal processing technique in which observed random data are transformed into components that are statistically independent from each other. this suggests the possibility of using ica to separate different independent brain activities during motor imagery into separate components. however, there is no guarantee for linear combination of brain sources in eeg signals. thus the identification of nonlinear dynamic of eeg signals should be taken into consideration. in this paper, a new method is proposed for eeg signal classification in bci systems by using nonlinear ica algorithm. the effectiveness of the proposed method is evaluated by using the classification of eeg signals. the tasks to be discriminated are the imaginative hand movement and the resting state. the results demonstrate that the proposed method performed well in several experiments on different subjects and can improve the classification accuracy in the bci systems.
time-space-sequential algorithms for distributed bayesian state estimation in serial sensor networks. we consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-gaussian state-space model. the current state is sensed by a serial sensor network without a fusion center. we present an optimal distributed bayesian estimation algorithm that is sequential both in time and in space (i.e., across sensors) and requires only local communication between neighboring sensors. for the linear/gaussian case, the algorithm reduces to a time-space-sequential, distributed form of the kalman filter. we also demonstrate the application of our state estimator to a target tracking problem, using a dynamically defined “local sensor chain” around the current target position.
new strategies for pronunciation by analogy. the synthesis quality is influenced by many important factors, among which the correctness of the grapheme-to-phoneme (g2p) conversion is one of the crucial ones. automatic letter-to-sound systems have been in the center of attention for the last decade. one of the most effective and promising methods resulted to be the so-called “pronunciation by analogy” method [8], based on the analogy in the grapheme context, allowing derivation of the correct pronunciation for a new word from the parts of similar words present in the dictionary. this paper aims at further development of this method. novel scoring strategies for determining the best pronunciations were proposed. a word error rate reduction of 1.5–2.5 percent is obtained. a detailed analysis shows that one of the new strategies consistently outperforms the others. the results obtained are compared to other g2p methods using the same data.
minimum generation error training by using original spectrum as reference for log spectral distortion measure. this paper improves a minimum generation error (mge) based hmm training technique for hmm-based speech synthesis by directly using the original spectrum instead of line spectral pairs (lsps) as reference spectrum for log spectral distortion (lsd) measure. two types of original reference spectra for lsd calculation are investigated, including the spectrum extracted from speech waveform by straight, and the short-time fft spectrum calculated from speech waveforms. since only the harmonics of the fft spectrum are coincident with the underlying spectral envelope, the lsd between generated lsps and original fft spectrum is calculated by sampling at the harmonic frequencies, and a weighting function is designed to simulate the sampling strategy on lsps. from the experimental results, the mge-lsd training using the fft spectrum as reference spectrum achieved the best performance.
distributed adaptive estimation of correlated node-specific signals in a fully connected sensor network. we introduce a distributed adaptive estimation algorithm operating in an ideal fully connected sensor network. the algorithm estimates node-specific signals at each node based on reduced-dimensionality sensor measurements of other nodes in the network. if the node-specific signals to be estimated are linearly dependent on a common latent process with a low dimension compared to the dimension of the sensor measurements, the algorithm can significantly reduce the required communication bandwidth and still provide the optimal linear estimator at each node as if all sensor measurements were available in every node. because of its adaptive nature and fast convergence properties, the algorithm is suited for real-time applications in dynamic environments, such as speech enhancement in acoustic sensor networks.
diffusion least-mean squares with adaptive combiners. this paper presents an efficient adaptive combination strategy for diffusion algorithms over adaptive networks in order to improve the robustness against the spatial variation of snr over the network. the diffusion least-mean square (lms) algorithm with the proposed combination rule and its mean transient analysis are included. simulation results show that the diffusion lms algorithm with our combiners outperforms those with existing static combiners and the incremental lms algorithm.
understanding the method of interval errors from the information theory perspective. nonlinear parameter estimation often displays a threshold phenomenon, that is, below certain signal-to-noise ratio (snr) the estimation mean-square error (mse) increases dramatically. the method of interval errors (mie) has been shown to provide accurate mse prediction of related nonlinear techniques well into the estimation threshold region, yet relatively simple and robust in evaluation compared to a global performance bound. however those features have not been understood on a strict theoretical basis. this paper investigates numerical sensitivity of the mie to parameter sampling resolution, aiming to understanding, from information theory perspective, the underlying mechanism leading to robust mse approximation. a recently-developed information theory resolution bound is reinterpreted and applied to specify the parameter sampling resolution. numerical evaluation of the relevant results for array-based bearing estimation supports the proposed connection between the resolution bound and the mie.
robust joint localization and time synchronization in wireless sensor networks with bounded anchor uncertainties. a unified framework to jointly solve the problems of localization and synchronization at the same time is presented in this paper. the joint approach is attractive because it can solve both localization and synchronization using the same set of message exchanges, which is extremely important for energy saving in wireless sensor networks. the inaccuracy of anchor locations and timings is taken into account to provide accurate joint localization and synchronization. the anchor uncertainties are assumed to be bounded, but knowledge of the statistics of anchor uncertainties is not required. the problem is formulated into a linear model with uncertainties on both sides of the equation. a robust joint estimator is then proposed based on minimizing the worst-case mean square error and the solution is obtained by solving a semidefinite programming problem. simulation results show that the proposed estimator outperforms the traditional least squares estimator at the cost of higher computational complexity.
centra catadioptric camera calibration with single image. central catadioptric camera is a class of omnidirectional sensor having single effective viewpoint with reflective surfaces and lenses. this work studies the geometry of central catadioptric projection of a set of lines and adopts it into calibration. in this paper, we propose a practical calibration method for central catadioptric cameras by manually labeling the corners on planar grids. this method analytically parameterizes the intersection of a set of lines, regardless whether they are parallel or not. algebraic distance is introduced in order to robustify this method since the conic which is the projection of line, is difficult to be obtained precisely. moreover, it is proved that central catadioptric is able to calibrate by at least one image with two lines. the performance of this method is validated by both simulation data and real image data.
automatic visual-only language identification: a preliminary study. we describe experiments in visual-only language identification, in which only lip-shape and lip-motion are used to determine the language of a spoken utterance. we focus on the task of discriminating between two or three languages spoken by the same speaker, and we have recorded a suitable database for these experiments. we use a standard audio language identification approach in which the feature vectors are tokenized and then a language model for each language is estimated over a stream of tokens. although rate of speaking appeared to affect our results, it was found that different languages spoken at rather similar speeds were as well discriminated as a single language spoken at three extreme speeds, indicating that there is a language effect present in our results.
on the importance of modeling temporal information in music tag annotation. music is an art form in which sounds are organized in time; however, current approaches for determining similarity and classification largely ignore temporal information. this paper presents an approach to automatic tagging which incorporates temporal aspects of music directly into the statistical models, unlike the typical bag-of-frames paradigm in traditional music information retrieval techniques. vector quantization on song segments leads to a vocabulary of acoustic segment models. an unsupervised, iterative process that cycles between viterbi decoding and baum-welch estimation builds transcripts of this vocabulary. latent semantic analysis converts the song transcriptions into a vector for subsequent classification using a support vector machine for each tag. experimental results demonstrate that the proposed approach performs better in 15 of the 18 tags. further analysis demonstrates an ability to capture local timbral characteristics as well as sequential arrangements of acoustic segment models.
joint estimation of signal and noise correlation matrices and its application to inverse filtering. noise suppression by linear filters for a time series is discussed. we propose a method for jointly estimating signal and noise correlation matrices by incorporating steering vectors of the noise or eigenvectors of the noise correlation matrix as well as steering vectors of the target signals. our estimates bring us two significant advantages. one is reduction of computational cost in obtaining the wiener filter since the wiener post filter, which is combined to the minimum variance distortionless response filter (mvdrf), is no longer needed with the estimates of signal and noise correlation matrices. the other is an improvement of the performance of the mvdrf since we can construct the regularized version of it with an estimate of the noise correlation matrix.
low-complexity frequency-domain turbo equalization for single-carrier transmissions over doubly-selective channels. single-carrier transmissions with frequency-domain equalization have gained much interest due to their comparable complexity and performance to ofdm, which conversely suffers from a high peak-to-average power ratio. in this paper, we develop a new frequency-domain block turbo equalizer for single-carrier (sc) transmissions over doubly-selective channels. the main feature of the proposed equalizer is its low complexity, which is only linear in the block length. a comparison between sc and ofdm systems with channel coding in doubly-selective channels is also given.