waashk/dblp huggingface dataset:
underdetermined audio source separation from anechoic mixtures with long time delay. we propose a technique to separate audio sources from their anechoic mixtures with long delay in an underdetermined setting (i.e., the number of audio sensors is smaller than that of sources). it consists of two stages: 1) to estimate anechoic mixing parameters of attenuation and arrival delay and 2) to recover original audio sources based on estimated mixing parameters. when delay is longer than one sample, previous algorithms perform poorly. to address this shortcoming, we estimate the maximum delay and use it to find a proper frequency range that produces no phase ambiguity. then, we determine mixing parameters with time-frequency points in this range. finally, mathematical tools are used to solve the underdetermined linear system to recover original audio sources. the effectiveness of the proposed technique on various mixing scenarios with noisy observation of mixtures and different types of sounds is demonstrated by experimental results.
a multistage approach for blind separation of convolutive speech mixtures. in this paper, we propose a novel algorithm for the separation of convolutive speech mixtures using two-microphone recordings, based on the combination of independent component analysis (ica) and ideal binary mask (ibm), together with a post-filtering process in the cepstral domain. essentially, the proposed algorithm consists of three steps. first, a constrained convolutive ica algorithm is applied to separate the source signals from two-microphone recordings. in the second step, we estimate the ibm by comparing the energy of corresponding time-frequency (t-f) units from the separated sources obtained with the convolutive ica algorithm. the last step is to reduce musical noise caused typically by t-f masking using cepstral smoothing. the performance of the proposed approach is evaluated based on both reverberant mixtures generated using a simulated room model and real recordings. the proposed algorithm offers considerably higher efficiency, together with improved speech quality while producing similar separation performance as compared with a recent approach.
inpainting with sparse linear combinations of exemplars. we introduce a new exemplar-based inpainting algorithm that represents the region to be inpainted as a sparse linear combination of example blocks, extracted from the image being inpainted or an external training image set. this method is conceptually simple, being computed by minimization of a simple functional, and avoids the complexity of correctly ordering the filling in of missing regions of other exemplar-based methods. initial performance comparisons on small inpainting regions indicate that this method provides similar or better performance than other recent methods.
target detection using incremental learning on single-trial evoked response. the human neural responses associated with cognitive events, referred as event related potentials (erps), can provide reliable inference for target image detection. incremental learning has been widely investigated to deal with large datasets. to solve the problem of data growing over time in cross session studies, we apply an incremental learning support vector machines (svm) method on single-trial erp detection for identifying targets in satellite images. we implement the incremental learning svm by keeping only the support vectors, instead of all the data, from the previous sessions and incorporating them with the data of the current session. thus the incremental learning dramatically reduces the computational load. the results demonstrate that the incremental learning erp detection system performs as well as the naive method, which uses only the current training session, and the batch mode, which uses all training data. furthermore, it is more computationally efficient, which allows it to better cope with a continuous stream of eeg data.
conditional random fields for the prediction of signal peptide cleavage sites. correct prediction of signal peptide cleavage sites has a significant impact on drug design. state-of-the-art approaches to cleavage site prediction typically use generative models (such as hmms) to represent the statistics of amino acid sequences or use neural networks to detect the changes in short amino-acid segments along a query sequence. by formulating cleavage site prediction as a sequence labeling problem, this paper demonstrates how conditional random fields (crfs) can be applied to cleavage site prediction. the paper also demonstrates how amino acid properties can be exploited and incorporated into the crfs to boost prediction performance. results show that the performance of crfs is comparable to that of a state-of-the-art predictor (signalp v3.0). further performance improvement was observed when the decisions of signalp and the crf-based predictor are fused.
a semi-supervised learning approach to online audio background detection. we present a framework for audio background modeling of complex and unstructured audio environments. the determination of background audio is important for understanding and predicting the ambient context surrounding an agent, both human and machine. our method extends the online adaptive gaussian mixture model technique to model variations in the background audio. we propose a method for learning the initial background model using a semi-supervised learning approach. this information is then integrated into the online background determination process, providing us with a more complete background model. we show that we can utilize both labeled and unlabeled data to improve audio classification performance. by incorporating prediction models in the determination process, we can improve the background detection performance even further. experimental results on real data sets demonstrate the effectiveness of our proposed method.
amplify-and-forward based cooperation for secure wireless communications. a physical layer approach to security for wireless networks is considered. in single-antenna wireless systems, such approaches are hampered by channel conditions in the presence of one or more eavesdroppers. cooperation has the potential to overcome this problem and improve the security of of wireless communications. in this paper, an amplify-and-forward based cooperative protocol is proposed. assuming availability of global channel state information, system design that maximizes the secrecy capacity is considered. since the optimal solution to this problem is intractable, suboptimal closed-form solutions are proposed that optimize bounds on secrecy capacity for the case of a single eavesdropper, or that introduce additional constraints, such as nulling of signals at all eavesdroppers, for the case of multiple eavesdroppers.
challenges in mobile network operation: towards self-optimizing networks. this paper reviews current status and trends in the application of self-organizing principles to advanced wireless networks, such as 3gpp long-term evolution (lte). the transfer of research results and concepts to real-world networks imposes additional constraints and requirements, which open a multitude of interesting new fields for applied research. particular challenges include defining appropriate assessment criteria, evaluation methodology, as well as a variety of interrelations between use cases with conflicting goals and mutual parameter dependencies yielding non-closed-form problems. furthermore only partial, error-prone and potentially inconsistent information is available. additional challenges include minimization of overhead, stability, and convergence issues, in particular for decentralized solutions.
model-based early termination scheme for h.264/avc inter prediction. inter prediction is the most power-consumed component in h.264/avc encoder. for the power-aware design, it is necessary to reduce the power consumption by using fast inter prediction techniques. in this paper, a system-level power-aware algorithm based on the early termination scheme of h.264/avc inter prediction is proposed. the proposed early termination scheme for h.264 motion estimation is based on the statistical modeling of the motion compensated residual data. we develop a power-aware adaptive mechanism with multiple thresholds derived from the statistical model to early terminate the motion estimation (me). according to the experimental results, our early termination scheme for h.264/avc inter prediction not only preserves fine rd performance but also eliminates the unnecessary operation in both ime stage and fme stage to realize the power-aware h.264 encoder system.
real-time dynamic mr image reconstruction using kalman filtered compressed sensing. in recent work, kalman filtered compressed sensing (kf-cs) was proposed to causally reconstruct time sequences of sparse signals, from a limited number of “incoherent” measurements. in this work, we develop the kf-cs idea for causal reconstruction of medical image sequences from mr data. this is the first real application of kf-cs and is considerably more difficult than simulation data for a number of reasons, for example, the measurement matrix for mr is not as “incoherent” and the images are only compressible (not sparse). greatly improved reconstruction results (as compared to cs and its recent modifications) on reconstructing cardiac and brain image sequences from dynamic mr data are shown.
receiver nonlinearity optimization in clipping channels. in this paper the effects of receiver nonlinearities are examined for clipping channels. the objective of this work is to determine the optimal receiver functions for additive noise clipping channels. in this case the optimal receiver function will be the one that maximizes the signal-to-noise-plus-distortion ratio (sndr) between the transmitted and received variables. to solve the problem we utilize functional analysis to find a necessary condition for the sndr-maximizing receiver function. the results are general and can be applied for any noise and signal distribution. furthermore, the results show that for the example given, the linear receiver is not sndr-optimal.
two dimensional maximum margin criterion. maximum margin criterion is a well-known method for feature extraction and dimensionality reduction. in this paper, we propose a novel feature extraction method, namely two dimensional maximum margin criterion (2dmmc), specifically for matrix representation data, e.g. images. 2dmmc aims to find two orthogonal projection matrices to project the original matrices to a low dimensional matrix subspace, in which a sample is close to those in the same class but far from those in different classes. both theoretical analysis and experiments on benchmark face recognition data sets illustrate that the proposed method is very effective and efficient.
generalized mutual interdependence analysis. the mean of a data set is one trivial representation of data from one class. recently, mutual interdependence analysis (mia) has been successfully used to extract more involved representations, or “mutual features”, accounting for samples in the class. for example a mutual feature is a speaker signature under varying channel conditions or a face signature under varying illumination conditions. a mutual representation is a linear regression that is equally correlated with all samples of the input class. we present the mia optimization criterion from the perspectives of regression, canonical correlation analysis and bayesian estimation. this allows us to state and solve the above criterion concisely, to contrast the mia solution to the sample mean, and to infer other properties of its closed form, unique solution under various statistical assumptions. we define a generalized mia solution (gmia) and apply mia and gmia in a text-independent speaker verification task using the ntimit database. both methods show competitive performance with equal-error-rates of 7.5 % and 6.5 % respectively over 630 speakers.
low-complexity sinusoidal component selection using loudness patterns. sinusoidal modeling of audio at low-bit rates involves selecting a limited number of parameters according to a quantitative or perceptual criterion. most perceptual sinusoidal component selection strategies are computationally intensive and not suitable for real-time applications. in this paper, a computationally efficient sinusoidal selection algorithm based on a novel hybrid loudness estimation scheme is presented. the hybrid scheme first estimates efficiently the loudness of a multi-tone signal from the loudness patterns of its constituent sinusoidal components. then it refines this estimate by performing a full evaluation of loudness but only in select critical bands. experimental results show that the proposed technique maintains a low perceptual sinusoidal synthesis error at a much lower computational complexity.
model-based non-linear estimation for adaptive image restoration. we propose a new image restoration algorithm that is driven by an adaptive piecewise autoregressive model (par). the strength of the new algorithm is its ability to preserve spatial structures better than its predecessors. the high adaptability is achieved by locally fitting 2d image waveform to the par model in moving windows. the problem is posed as one of nonlinear least-square estimation of both par parameters and original pixels, constrained by the degradation function. robust solutions of the underlying underdetermined inverse problem are obtained by an innovative use of multiple par models that circumvent the issue of model overfitting, and by applying a structured total least-square technique.
web-derived pronunciations. pronunciation information is available in large quantities on the web, in the form of ipa and ad-hoc transcriptions. we describe techniques for extracting candidate pronunciations from web pages and associating them with orthographic words, filtering out poorly extracted pronunciations, normalizing ipa pronunciations to better conform to a common transcription standard, and generating phonemic from ad-hoc transcriptions. we show improvements on a letter-to-phoneme task when using web-derived vs. pronlex pronunciations.
multiterminal source coding of bernoulli-gaussian correlated sources. this paper presents a practical coding scheme for the direct symmetric multiterminal source coding problem with remote source, when the noise between the remote source and the observations is gaussian-bernoulli-gaussian. the idea behind the design is to take advantage from the observed symbols being in the real field, in order to perform low-dimensional compressed sensing. the coding scheme is based on channel coding techniques involving bch codes over the real field. simulations with respect to the rate distortion performance are provided, along with insights about optimization issues. robustness to variations of the probability of impulse is also investigated. perspectives about the application to the ceo problem in presence of gaussian-bernoulli-gaussian correlation noise between the remote source and the observations conclude the paper.
multi-antenna cognitive radio systems: environmental learning and channel training. this paper presents a multi-antenna cognitive radio (cr) system that is capable of operating concurrently with the primary radio (pr) link. the operation of the cr system consists of three stages: environmental learning, cr channel training and cr data transmission. in environmental learning stage, partial channel information between pr and cr are obtained blindly, based on which the transmit beamforming and the receive beamforming strategies are designed at cr to remove/reduce the interference to and from pr, respectively. we characterize all the interference values analytically and study the problem of learning/training tradeoff associated with the proposed scheme. the optimal balancing between learning and training is examined via the minimum mean square error (mse) of the channel estimation. it is shown that for a given total learning/training time, there indeed exists a optimal learning time that minimizes the mse of the channel estimation, yet the interference power to the pr is regulated.
multiuser mimo-mac capacity in the low snr regime: channel knowledge and double-scattering. we investigate the sum capacity of the uplink multiuser multiple-input multiple-output (mimo) multiple-access channel (mac) in the low signal-to-noise ratio (snr) regime. for each user, the mimo channel is modeled according to a general class of stochastic channel matrices, known as double-scattering. assuming that each user knows only their own spatial correlation matrices and employs optimal statistical beamforming transmission, we present new analytical approximations for the sum capacity of the mimo-mac for low snr values. our approximations are accurate, and lead to key insights into the effect of correlation.
on the effect of transmitter iq imbalance at ofdma receivers. one limiting issue in implementing high-speed wireless systems is the impairment associated with analog processing due to component imperfections. in uplink transmission of multiuser systems, a major source of such impairment is iq imbalance (iqi) introduced at multiple transmitters. in this paper, we analyze the effect of the transmitter (tx) iqi on ofdma receiver. to cope with the inter-user interference problem due to tx iqi, we propose a novel subcarrier allocation scheme, which has high tolerance to such tx iq distortion.
reduced-complexity delay-doppler correlator for time-frequency hopping signals. the delay-doppler correlator is most commonly used for target detection in a radar system. it is essentially a two-dimensional filter matched to the hypothesized delay and doppler shifts of echoes reflected from the illuminating signal. more recently, it has been applied in communication to the detection of signature sequences generated from artificially introduced time-frequency shifts of a base sequence. in this paper, we derive a computationally efficient delay-doppler correlator for a class of time-frequency hopping waveforms that can be represented by segments of equal-length sinusoids. these sequences can be found in applications such as continuous waveform radar and device identification in wireless communication. by applying sliding discrete fourier transform and exploiting the structure of the waveform, the number of multiplications required for evaluating the entire delay-doppler range can be reduced by a factor equaling the length of the sequence. 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
factor graph based design of an ofdm-idma receiver performing joint data detection, channel estimation, and channel length selection. we present a factor graph based design of a receiver for pilot-assisted ofdm-idma systems transmitting over frequency-selective channels. the receiver performs joint iterative multiuser data detection and channel estimation with a complexity that is linear in the number of users, and it includes estimation of the channel length. simulation results demonstrate large performance gains compared to ofdm-idma receivers using separate mmse channel estimation.
sparse source separation from orthogonal mixtures. this paper addresses source separation from a linear mixture under two assumptions: source sparsity and orthogonality of the mixing matrix. we propose efficient sparse separation via a two-stage process. in the first stage we attempt to recover the sparsity pattern of the sources by exploiting the orthogonality prior. in the second stage, the support is used to reformulate the recovery task as an optimization problem. we then suggest a solution based on alternating minimization. random simulations are performed to analyze the behavior of the resulting algorithm. the simulations demonstrate convergence of our approach as well as superior recovery rate in comparison with alternative source separation methods and k-svd, a leading algorithm in dictionary learning.
a new method for oov detection using hybrid word/fragment system. in this paper, we propose a new method for detecting regions with out-of-vocabulary (oov) words in the output of a large vocabulary continuous speech recognition (lvcsr) system. the proposed method uses a hybrid system combining words and data-driven variable length sub word units. with the use of a single feature, the posterior probability of sub word units, this method outperforms existing methods published in the literature. we also presents a recipe to discriminatively train a hybrid language model to improve oov detection rate. results are presented on the rt04 broadcast news task.
advances in syntax-based malay-english speech translation. in this paper, we present advanced techniques that improved the performance of ibm malay-english speech translation system significantly. during this work, we generated linguistics-driven hierarchical rules to enhance the formal syntax-based translation model; designed an active learning approach with bi-directional translations that outperformed unsupervised training; utilized translation direction information in parallel training corpus to build direction-specific interpolated language models for machine translation. there is 20% relative improvement achieved in the translation performance through all these techniques. a state-of-the-art malay speech recognition system was also established as one of the crucial modules in the rapidly developed malay-english speech translation.
sparse multivariate autoregressive (mar)-based partial directed coherence (pdc) for electroencephalogram (eeg) analysis. partial directed coherence (pdc) has recently been proposed for studying brain connectivity in eeg studies. pdc provides a quantitative spectral measure of the causal relations between signals by its central use of a multivariate autoregressive (mar) model. yet, in real applications, the successful estimation of pdc depends on the accuracy of mar parameter estimation, which is often sensitive to the data size and model order. in addition, it is generally believed that connections between eeg nodes (brain regions) may be sparse. to address these concerns, we propose a sparse mar-based pdc technique where pdc estimates are computed from sparse mar coefficient matrices derived from penalized regression. the proposed technique is applied to both simulated data and real eeg recordings, and results show enhanced stability and accuracy of the proposed technique compared to the traditional, non-sparse approach. the sparse mar-based pdc technique is promising for analyzing brain connectivity in eeg analysis.
fishervoice and semi-supervised speaker clustering. speaker subspace modeling has become increasingly important in speaker recognition, diarization, and clustering. principal component analysis (pca) is a popular linear subspace learning technique and the approach that represents an arbitrary utterance or speaker as a linear combination of a set of basis voices based on pca is known as the eigenvoice approach. in this paper, a novel technique, namely the fishervoice approach, is proposed. the fishervoice approach is based on linear discriminant analysis, another successful linear subspace learning technique that provides an optimized low-dimensional representation of utterances or speakers with focus on the most discriminative basis voices. we apply the fishervoice approach to speaker clustering in a semi-supervised manner and show that the fishervoice approach significantly outperforms the eigenvoice approach in all our experiments on the gale mandarin dataset.