waashk/dblp huggingface dataset:
role of head pose estimation in speech acquisition from distant microphones. reverberant environments pose a challenge to speech acquisition from distant microphones. approaches using microphone arrays have met with limited success. recent research using audio-visual sensors for tasks such as speaker localization has shown improvement over traditional audio-only approaches. using computer vision techniques we can estimate the orientation of the speaker's head in addition to the location of the speaker. in this paper we study the utility of using the head pose information for effective beamforming and clean speech acquisition from distant microphones. the improvements in speech recognition accuracy relative to that of a close talking microphone are presented and the results provide sufficient motivation for incorporating head pose information in beamforming techniques.
qualitative analysis of rotational modes within three dimensional empirical mode decomposition. an analysis of quaternion-valued intrinsic mode functions (imfs) within three dimensional empirical mode decomposition is presented. this is achieved by using the delay vector variance (dvv) method, which examines the signal predictability in phase space to assess the determinism and nonlinearity within the signal. the study illustrates that the contribution of the first few imfs contain information related to the stochastic/nonlinear signal nature, whereas the lower order imfs are largely deterministic. the analysis is supported by simulation results on a quaternion signal composed of linear/nonlinear benchmark signals and on real world wind data.
a split quaternion nonlinear adaptive filter. a split quaternion learning algorithm for the training of nonlinear finite impulse response filters for the modelling of hypercomplex signals is proposed. a rigorous derivation takes into account the non-commutativity of the quaternion product, an aspect not taken into account in the existing nonlinear architectures, such as the quaternion multilayer perceptron (qmlp). it is shown that the additional information present within the proposed algorithm provides an improved performance over qmlp. simulation on both benchmark and real-world signals support the approach.
robust and fast non asymmetric distributed source coding using turbo codes on the syndrome trellis. we consider the distributed compression of two (binary memoryless) correlated sources and propose a unique codec that can reach any point in the slepian-wolf region. in a previous method based on channel codes, the decoder multiply the compressed data by an inverse submatrix of the code. this multiplication presents two drawbacks. first, if turbo codes are used, the submatrix has no periodic structure s.t. the whole inverse has to be stored and no fast implementation exists for the multiplication. second, this multiplication may lead to error propagation. in this paper, we propose a method that is both robust and fast.
optimal widely linear mvdr beamforming for noncircular signals. this paper introduces the optimal widely linear (wl) minimum variance distorsionless response (mvdr) beamformer for the reception of an unknown signal of interest (soi) corrupted by potentially second order (so) noncircular background noise and interference. the soi, whose waveform is unknown, is assumed to be so noncircular with arbitrary noncircular properties. in the steady state and for so noncircular soi and/or interference, this new wl beamformer, that is derived from an original orthogonal decomposition, is shown to always improve the performance of both the well-known capon's beamformer and a wl mvdr beamformer introduced recently in the literature. this optimal wl mvdr beamformer is first introduced and some of its performance are analyzed. then, several adaptive implementations of this optimal wl beamformer are presented.
video event detection and summarization using audio, visual and text saliency. detection of perceptually important video events is formulated here on the basis of saliency models for the audio, visual and textual information conveyed in a video stream. audio saliency is assessed by cues that quantify multifrequency waveform modulations, extracted through nonlinear operators and energy tracking. visual saliency is measured through a spatiotemporal attention model driven by intensity, color and motion. text saliency is extracted from part-of-speech tagging on the subtitles information available with most movie distributions. the various modality curves are integrated in a single attention curve, where the presence of an event may be signified in one or multiple domains. this multimodal saliency curve is the basis of a bottom-up video summarization algorithm, that refines results from unimodal or audiovisual-based skimming. the algorithm performs favorably for video summarization in terms of informativeness and enjoyability.
on the performance of semidefinite relaxation mimo detectors for qam constellations. due to their computational efficiency and strong empirical performance, semidefinite relaxation (sdr)-based algorithms have gained much attention in multiple-input multiple-output (mimo) detection. in the case of a binary phase-shift keying (bpsk) constellation, the theoretical performance of the sdr approach is relatively well-understood. however, little is known about the case of quadrature amplitude modulation (qam) constellations, although simulation results suggest that the sdr approach should work well in the low signal-to-noise ratio (snr) region. in this paper we make a first step towards explaining such phenomenon by showing that in the case of qam constellations, several commonly used sdr-based algorithms will provide a constant factor approximation to the optimal log-likelihood value in the low snr region with exponentially high probability. our result gives some theoretical justification for using sdr-based algorithms for the mimo detection of qam signals, at least in the low snr region.
incorporating prior knowledge on the digital media creation process into audio classifiers. in the process of music content creation, a wide range of typical audio effects such as reverberation, equalization or dynamic compression are very commonly used. despite the fact that such effects have a clear impact on the audio features, they are rarely taken into account when building an automatic audio classifier. in this paper, it is shown that the incorporation of prior knowledge of the digital media creation chain can clearly improve the robustness of the audio classifiers, which is demonstrated on a task of musical instrument recognition. the proposed system is based on a robust feature selection strategy, on a novel use of the virtual support vector machines technique and a specific equalization used to normalize the signals to be classified. the robustness of the proposed system is experimentally evidenced using a rather large and varied sound database.
new insights into non-causal multichannel linear filtering for noise reduction. we investigate a general framework for noise reduction which consists in controlling the level of signal distortion while reducing the level of noise. a parameterized non-causal filter that allows for tuning the signal distortion and noise reduction inversely is obtained and is referred to as parameterized multichannel non-causal wiener filter (pmwf) herein. the same optimization problem leads to the minimum variance distortionless response (mvdr) as a particular case of the pmwf. in contrast to earlier works, the proposed expressions of the pmwf and mvdr are simplified and require the knowledge of the speech and noise statistics only. to rigorously quantify the gains and losses when using these filters, we establish simplified closed-form expressions for three measures, namely, the signal distortion index, the noise reduction factor, and the output signal-to-noise ratio (snr), and highlight the tradeoff between noise reduction and speech distortion in the multichannel case.
a new robust estimation method for arma models. this paper presents a new robust method to estimate the parameters of arma models. this method makes use of the autocorrelations estimates based on the ratio of medians together with a robust filter cleaner able to reject a large fraction of outliers, and a gaussian maximum likelihood estimation which handles missing values. the main advantages of the procedure are its easiness, robustness and fast execution. its effectiveness is demonstrated on an example of the forecasting of the french daily electricity consumptions.
structured spectrum balancing in dsl multiuser communications. finding the power allocations that maximize the sum-rate of a k-user n-tone digital subscriber line (dsl) system is known to be np-hard. in this paper we devise a polynomial-time algorithm to approximate the maximum sum rate of the system. the development of this algorithm is guided by the fact that, to approach the sumrate maximum, the users should operate in an fdma-mode over frequency tones where the crosstalk coefficients exceed a certain threshold, and should share the tones for which the crosstalk coefficients are sufficiently small. drawing on this insight, the algorithm partitions the n tones into three sections and imposes an appropriate signalling structure on each section. the first section contains those tones for which the crosstalk coefficients are small and uses an iterative water-filling technique to determine the power allocations. the second section contains the tones with intermediate crosstalk coefficients and uses a primal-dual algorithm, and the third section contains the tones with large crosstalk coefficients and uses a dual fdma algorithm. to decouple the overall optimization of power allocation across the three sections, we use tools from lagrangian duality and sensitivity analysis to devise an iterative scheme that can optimally allocate each user's power budget to the three sections. our numerical simulations, show that the sum-rate of the proposed algorithm is very close to that of the ‘optimal’ spectrum balancing algorithm, but requires considerably less computational effort.
layers and layer interfaces in wireless networks. this paper proposes an optimal architecture for wireless networks based on layers and layer interfaces. in the presence of fading the architecture is shown to be optimal. the result follows from a subgradient descent algorithm on the dual function of a generic wireless networking optimization problem. the fact that these non-convex optimization problems have nonetheless zero duality gap is exploited.
adaptive predistortion of nonlinear volterra systems using spectral magnitude matching. digital compensation of nonlinear systems is an important topic in many practical applications. this paper considers the problem of predistortion of nonlinear systems described using volterra series by connecting in tandem an adaptive volterra predistorter. the suggested direct learning architecture (dla) approach utilizes the spectral magnitude matching (smm) method that minimizes the sum squared error between the spectral magnitudes of the output signal of the nonlinear system and the desired signal. the coefficients of the predistorter are estimated recursively using the generalized newton iterative algorithm. a comparative simulation study with the nonlinear filtered-x least mean squares (nfxlms) algorithm shows that the suggested smm approach achieves much better performance but with higher computation complexity.
recursive errors-in-variables approach for ar parameter estimation from noisy observations. application to radar sea clutter rejection. ar modeling is used in a wide range of applications from speech processing to rayleigh fading channel simulation. when the observations are disturbed by an additive white noise, the standard least squares estimation of the ar parameters is biased. some authors of this paper recently reformulated this problem as an errors-in-variables (eiv) issue and proposed an off-line solution, which outperforms other existing methods. nevertheless, its computational cost may be high. in this paper, we present a blind recursive eiv method that can be implemented for real-time applications. it has the advantage of converging faster than the noise-compensated lms based solutions. in addition, unlike ekf or sigma point kalman filter, it does not require a priori knowledge such as the variances of the driving process and the additive noise. the approach is first tested with synthetic data; then, its relevance is illustrated in the field of radar sea clutter rejection.
a class of comprehensive constraints for the pcwlse filter design: a boost in performance. the complex chebyshev error criterion is usually used as a general constraint in design of peak constraint weighted least square error (pcwlse) filters. it applies an upper bound on the maximum magnitude of error between the desired and designed transfer functions of the filter. therefore, it confines the corresponding maximum phase error as well. however, it imposes an over restricted constraint which reduces the feasibility region of the filter design problem. in this paper, a new and comprehensive class of constraints is proposed for design of pcwlse filters that provides a larger feasible region than that of the complex chebyshev constraint. hence, the filter weights acquire larger feasible space to search and select better optimal values and consequently boost up the performance of the designed filter by achieving less weighted least square error. simulation results show superiority of the proposed constraints over that of the complex chebyshev criterion.
on-line adaptation and bayesian detection of environmental changes based on a macroscopic time evolution system. acoustic characteristics are often changed over time as a result of various factors including changes of speakers, speaking styles, and noise sources. incremental adaptation techniques for speech recognition are aimed at adjusting acoustic models quickly and stably to such time-variant acoustic characteristics. recently we proposed a novel incremental adaptation framework based on a macroscopic time evolution system, which models the time-variant characteristics by successively updating posterior distributions of acoustic model parameters. this paper proposes fast incremental adaptation based on a macroscopic time evolution system that realizes an utterance-by-utterance update by approximating the posterior distributions. this adaptation was used to perform on-line adaptation of japanese broadcast news for very large vocabulary continuous speech recognition (700k vocabulary size) in real time. the word accuracy was improved from 73.9% to 85.1%. in addition, by incorporating a bayesian model selection approach, we realized the simultaneous on-line adaptation and detection of environmental changes.
modifications to the sliding-window kernel rls algorithm for time-varying nonlinear systems: online resizing of the kernel matrix. a kernel-based recursive least-squares algorithm that implements a fixed size “sliding-window” technique has been recently proposed for fast adaptive nonlinear filtering applications. we propose a methodology of resizing the kernel matrix to assist in system identification of time-varying nonlinear systems. to be applicable in practice, the modified algorithm must preserve its ability to operate online. given a bound on the maximum kernel matrix size, we define the set of all obtainable sizes as the resizing range. we then propose a simple online technique that resizes the kernel matrix within the resizing range. the modified algorithm is applied to the nonlinear system identification problem that was used to evaluate the original algorithm. results show that an increase in performance is achieved without increasing the original algorithm's computation time.
improving acoustic speaker verification with visual body-language features. we show how an svm based acoustic speaker verification system can be significantly improved in incorporating new visual features that capture the speaker's “body language.” we apply this system to many hours of internet videos and tv broadcasts of politicians and other public figures. our data ranges from current and former us election candidates to the queen of england, the president of france, and the pope, while giving speeches.
room impulse response shortening with infinity-norm optimization. the purpose of room impulse response (rir) shortening is to improve the intelligibility of the received signal by prefiltering the source signal before it is played with a loudspeaker in a closed room. in this paper, we propose to use the infinity-norm as optimization criterion for the design of shortening filters of rirs. similar to the equiripple filter design method, design errors will be uniformly distributed over the unwanted temporal range of the shortened global impulse response. the d50 measure is exploited during the design of the shortening filter, which makes it possible to significantly reduce the length of the prefilter without affecting the perceived performance.
optimal variable fractional delay filters in time-domain l-infinity norm. this paper presents an efficient implementation of the variable fractional-delay (vfd) filter, which is optimal in the time-domain l-infinity norm. the proposed filter has two stages. the first consist in computing the conventional lagrange interpolator from the signal samples, but weighted using a set of fixed coefficients. and the second consists in multiplying the result of the previous step by a smooth function, which can be well approximated by a polynomial. the paper includes a numerical evaluation of this interpolator and a low-complexity, low-latency implementation based on multiplications. 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
using information theory to detect voice activity. voice activity detection systems attempt to discriminate between voice and other ambient sounds. most systems use a single microphone approach and rely on training prior to employment. the performance of these systems relies heavily on reverberation and noise levels. in this paper we present an unsupervised voice activity detection system that uses pairs of microphones to discern between a coherent acoustic source and spatially diffuse noise of low coherence. measurement of coherency is performed using an information theoretic metric that integrates means to filter out more effectively the effect of reverberation and noise. using extensive experiments, the performance of the system is investigated. based on the conditions imposed by the experimental environments it is shown that the proposed system remains more robust than its counterparts in all cases.
multivariate spatial gaussian mixture modeling for statistical clustering of hemodynamic parameters in functional mri. in this paper, a novel statistical parcellation of intra-subject functional mri (fmri) data is proposed. the key idea is to identify functionally homogenous regions of interest from their hemodynamic parameters. to this end, a non-parametric voxel-based estimation of hemodynamic response function is performed as a prerequisite. then, the extracted hemodynamic features are entered as the input data of a multivariate spatial gaussian mixture model (msgmm) to be fitted. the goal of the spatial aspect is to favor the recovery of connected components in the mixture. our statistical clustering approach is original in the sense that it extends existing works done on univariate spatially regularized gaussian mixtures [1]. a specific gibbs sampler is derived to account for different covariance structures in the feature space. on realistic artificial fmri datasets, it is shown that our algorithm is helpful for identifying a parsimonious functional parcellation required in the context of joint detection-estimation of brain activity [2]. this allows us to overcome the classical assumption of spatial stationarity of the bold signal model.
distributed sampling and reconstruction of non-bandlimited fields in sensor networks based on shift-invariant spaces. we use the theory and algorithms developed for so-called shift-invariant spaces to develop a novel distributed architecture for sampling and reconstructing non-bandlimited fields in wireless sensor networks. our scheme groups neighboring sensors into clusters that locally perform highly accurate field reconstruction with limited communication overhead. the overall complexity of our method scales only linearly with the number of sensors. numerical simulations illustrate that the proposed field reconstruction scheme outperforms band-limited reconstruction, even though the latter has much larger complexity.
single-microphone late-reverberation suppression in noisy speech by exploiting long-term correlation in the dft domain. we consider blind late-reverberation suppression in speech signals measured with a single microphone in noisy environments. we exploit that reverberant speech shows correlation over longer time spans than clean speech by predicting the contribution of reverberant energy to the current observed spectrum from the enhanced spectra of previous frames. the prediction parameters are recursively updated with estimates of the correlation coefficients between the current reverberant spectrum and enhanced previous spectra. the contributions of late reverberation and noise are suppressed by a standard noise reduction algorithm. the algorithm is shown to decrease the long-term correlation. it achieves significant improvements in segmental speech-to-interference ratio and bark spectral distortion for typical reverberation times and noise levels, while almost no distortions are introduced in clean speech.
sensor fusion method using gps/imu data for fast uav surveillance video frame registration. this paper proposes an innovative framework for fast image registration of uav surveillance video frames by fusing the data from a gps receiver high-frequency imu sensor (piccolo autopilot) and a feature-domain registration method through a non-linear filter. the high-frequency imprecise data from the piccolo autopilot is refined by the low-frequency precise data from our feature-domain based random m least squares (rmls) method. the projective transformation model is chosen to achieve high precision. the state and measurement models are non-linear to approximate the real-world imaging dynamics. a periodic hybrid particle filter (phpf), composed of extended kalman filter (ekf) and unscented kalman filter (ukf), is proposed to minimize running time while maintaining accuracy. both the efficiency and effectiveness of the proposed algorithm will be evaluated through our experiments.
a bayesian approach to hmm-based speech synthesis. this paper proposes a new framework of speech synthesis based on the bayesian approach. the bayesian method is a statistical technique for estimating reliable predictive distributions by marginalizing model parameters. in the proposed framework, all processes for constructing the system can be derived from one single predictive distribution which represents the basic problem of speech synthesis directly. using hmm as the likelihood function and assuming some approximations, it can be regarded as an application of the variational bayesian method to the hmm-based speech synthesis. experimental results show that the proposed method outperforms the conventional one in a subjective test.