Showing papers on "Impulse response published in 2015"

Macroeconomics and the Reality of Mixed Frequency Data

Abstract: Many time series are sampled at different frequencies. When we study co-movements between such series we usually analyze the joint process sampled at a common low frequency. This has consequences in terms of potentially mis-specifying the comovements and hence the analysis of impulse response functions - a commonly used tool for economic policy analysis. We introduce a class of mixed frequency VAR models that allows us to measure the impact of high frequency data on low frequency and vice versa. Our approach does not rely on latent processes/shocks representations. As a consequence, the mixed frequency VAR is an alternative to commonly used state space models for mixed frequency data. State space models are parameter-driven whereas mixed frequency VAR models are observation-driven models as they are formulated exclusively in terms of observable data and do not involve latent processes as well as shocks and thus avoid the need to formulate measurement equations, filtering etc. We also propose various parsimonious parameterizations, in part inspired by recent work on MIDAS regressions. We also explicitly characterize the mis-specification of a traditional common low frequency VAR and its implied mis-specified impulse response functions. The class of mixed frequency VAR models can also characterize the timing of information releases for a mixture of sampling frequencies and the real-time updating of predictions caused by the flow of high frequency information. Various estimation procedures for mixed frequency VAR models are also proposed, both classical and Bayesian. Numerical and empirical examples quantify the consequences of ignoring mixed frequency data.

Customizing speech-recognition dictionaries in a smart-home environment

Abstract: A method for customizing speech-recognition dictionaries for different smart-home environments may include generating, at a smart-home device mounted in an enclosure, an acoustic impulse response for the enclosure The method may also include receiving, by the smart-home device, an audio signal captured in the enclosure The method may additionally include performing, by the smart-home device, a speech-recognition process on the audio signal using a second speech dictionary generated by convolving the acoustic impulse response with a first speech dictionary

Frequency and Time Domain Channel Models for Nanonetworks in Terahertz Band

Abstract: Time and frequency domain channel models are proposed for nanonetworks utilizing the terahertz band (0.1–10 THz) for wireless communication. Nanonetworks are formed by tiny nanodevices which consist of nanoscale (molecular scale) components. Channel models capturing the unique peculiarities of the THz band are needed for designing proper physical layer techniques and for accurate performance analysis. Existing channel models have included the free space path loss and the molecular absorption loss, which is significant in the THz band. This paper theoretically analyzes scattering including multiple scattering referring to a sequence of scattering events from small particles, such as aerosols. Both the frequency and the impulse responses are derived. It is shown that the small particle scattering can result into significant additional loss that needs to be taken into account with the loss depending on the density and size distribution of the particles. It is shown that multiple scattering leads to a long tail in the impulse response. As most of the physical layer proposals for nanonetworks are based on the on-off keying, the channel response to pulse waveforms is specifically considered.

Stationary‐phase integrals in the cross correlation of ambient noise

Abstract: The cross correlation of ambient signal allows seismologists to collect data even in the absence of seismic events. “Seismic interferometry” shows that the cross correlation of simultaneous recordings of a random wavefield made at two locations is formally related to the impulse response between those locations. This idea has found many applications in seismology, as a growing number of dense seismic networks become available: cross-correlating long seismic records, the Green's function between instrument pairs is “reconstructed” and used, just like the seismic recording of an explosion, in tomography, monitoring, etc. These applications have been accompanied by theoretical investigations of the relationship between noise cross correlation and the Green's function; numerous formulations of “ambient noise” theory have emerged, each based on different hypotheses and/or analytical approaches. The purpose of this study is to present most of those approaches together, providing a comprehensive overview of the theory. Understanding the specific hypotheses behind each Green's function recipe is critical to its correct application. Hoping to guide nonspecialists who approach ambient noise theory for the first time, we treat the simplest formulation (the stationary-phase approximation applied to smooth unbounded media) in detail. We then move on to more general treatments, illustrating that the “stationary-phase” and “reciprocity theorem” approaches lead to the same formulae when applied to the same scenario. We show that a formal cross correlation/Green's function relationship can be found in complex, bounded media and for nonuniform source distributions. We finally provide the bases for understanding how the Green's function is reconstructed in the presence of scattering obstacles.

Statistical Channel Model with Multi-Frequency and Arbitrary Antenna Beamwidth for Millimeter-Wave Outdoor Communications

Abstract: This paper presents a 3-dimensional millimeter- wave statistical channel impulse response model from 28 GHz and 73 GHz ultrawideband propagation measurements. An accurate 3GPP-like channel model that supports arbitrary carrier frequency, RF bandwidth, and antenna beamwidth (for both omnidirectional and arbitrary directional antennas), is provided. Time cluster and spatial lobe model parameters are extracted from empirical distributions from field measurements. A step-by- step modeling procedure for generating channel coefficients is shown to agree with statistics from the field measurements, thus confirming that the statistical channel model faithfully recreates spatial and temporal channel impulse responses for use in millimeter-wave 5G air interface designs.

Design and Simulation

Abstract: transducers made of 64 0 YZ LiNbO3 at a frequency of 400 MHz are studied and analyzed with the help of MATLAB. Design formulation are based on a two dimensional piezoelectric transducers operating at 400 MHz The frequency dependence of Radiation Conductance and Acoustic Susceptance are studied with the help of Impulse Response method. The impulse response method is used for the calculation of transducer input admittance and filter frequency response with much less computational effort than required by earlier approaches. A high quality factor with very low insertion loss is obtained.

Synchronized Swept-Sine: Theory, Application, and Implementation

Abstract: Exponential, or sometimes called logarithmic, swept-sine signal is very often used to analyze nonlinear audio systems. In this paper, the theory of exponential swept-sine measurements of nonlinear systems is reexamined. The synchronization procedure necessary for a proper analysis of higher harmonics is detailed leading to an improvement of the formula for the exponential swept-sine signal generation. Moreover, an analytical expression of spectra of the swept-sine signal is derived and used in the deconvolution of the impulse response. A Matlab code for generation of the synchronized swept-sine, deconvolution, and separation of the impulse responses is given with discussion of some application issues and an illustrative example of harmonic analysis of current distortion of a woofer is provided.

Kernels for Linear Time Invariant System Identification

Abstract: In this paper, we study the problem of identifying the impulse response of a linear time invariant (LTI) dynamical system from the knowledge of the input signal and a finite set of noisy output observations. We adopt an approach based on regularization in a reproducing kernel Hilbert space (RKHS) that takes into account both continuous- and discrete-time systems. The focus of the paper is on designing spaces that are well suited for temporal impulse response modeling. To this end, we construct and characterize general families of kernels that incorporate system properties such as stability, relative degree, absence of oscillatory behavior, smoothness, or delay. In addition, we discuss the possibility of automatically searching over these classes by means of kernel learning techniques, so as to capture different modes of the system to be identified.

Intensity impulse response of SDM links.

Abstract: We study the response of space-division multiplexed fiber links to an excitation by a short impulse of the optical intensity. We show that, in the presence of full mixing, the intensity impulse response is Gaussian, confirming recently reported experimental observations, and relate its variance to the mean square of the mode dispersion vector of the link τ. The good agreement between our theory and the previously published experiments provides solid foundations to the random coupling model of SDM fiber links, and provides a tool for efficient design of MIMO-DSP receivers.

Comparative analysis of the methods to compute the radiation term in Cummins’ equation

Abstract: In the present paper, the methods provided in literature to compute the convolution integral in Cummins’ equation are compared. Direct computation of the convolution integral is revised to avoid truncation errors and to save computational cost. The three methods compared are the direct computation of the convolution integral, the approximation of the integral by a state space and the approximation of the impulse response function by Prony’s coefficients. These methods are used to simulate the movement of the water inside an oscillating water column (OWC) and a decay test in heave of a spar buoy. Cummins’ equation results in a system of ordinary differential equations with all the methods. All systems are computed using the same numerical scheme obtaining a fair comparison of the computational cost involved in each method. The results of the OWC are compared against CFD results and the results of the buoy against laboratory experiments. Results obtained by direct computation of the convolution integral show sensitivity to the time step used to precompute the impulse response function, while using state space or Prony’s approximations are dependent on the set of frequencies required for the identification of their coefficients. State space and Prony’s approximations evaluate the radiation force, including it in the matrix of the system, while direct integration computes it outside of the matrix. This modification in the matrix makes these approximations more sensitive to the data used to evaluate the radiation force.

Regularized system identification using orthonormal basis functions

Abstract: Most of existing results on regularized system identification focus on regularized impulse response estimation. Since the impulse response model is a special case of orthonormal basis functions, it is interesting to consider if it is possible to tackle the regularized system identification using more compact orthonormal basis functions. In this paper, we explore two possibilities. First, we construct reproducing kernel Hilbert space of impulse responses by orthonormal basis functions and then use the induced reproducing kernel for the regularized impulse response estimation. Second, we extend the regularization method from impulse response estimation to the more general orthonormal basis functions estimation. For both cases, the poles of the basis functions are treated as hyper-parameters and estimated by empirical Bayes method. Then we further show that the former is a special case of the latter, and more specifically, the former is equivalent to ridge regression of the coefficients of the orthonormal basis functions.

Spatial source subtraction based on incomplete measurements of relative transfer function

Abstract: Relative impulse responses between microphones are usually long and dense due to the reverberant acoustic environment. Estimating them from short and noisy recordings poses a long-standing challenge of audio signal processing. In this paper, we apply a novel strategy based on ideas of compressed sensing. Relative transfer function (RTF) corresponding to the relative impulse response can often be estimated accurately from noisy data but only for certain frequencies. This means that often only an incomplete measurement of the RTF is available. A complete RTF estimate can be obtained through finding its sparsest representation in the time-domain: that is, through computing the sparsest among the corresponding relative impulse responses. Based on this approach, we propose to estimate the RTF from noisy data in three steps. First, the RTF is estimated using any conventional method such as the nonstationarity-based estimator by Gannot et al. or through blind source separation. Second, frequencies are determined for which the RTF estimate appears to be accurate. Third, the RTF is reconstructed through solving a weighted l1 convex program, which we propose to solve via a computationally efficient variant of the SpaRSA (Sparse Reconstruction by Separable Approximation) algorithm. An extensive experimental study with real-world recordings has been conducted. It has been shown that the proposed method is capable of improving many conventional estimators used as the first step in most situations.

Bayesian Blind Separation and Deconvolution of Dynamic Image Sequences Using Sparsity Priors

Abstract: A common problem of imaging 3-D objects into image plane is superposition of the projected structures. In dynamic imaging, projection overlaps of organs and tissues complicate extraction of signals specific to individual structures with different dynamics. The problem manifests itself also in dynamic tomography as tissue mixtures are present in voxels. Separation of signals specific to dynamic structures belongs to the category of blind source separation. It is an underdetermined problem with many possible solutions. Existing separation methods select the solution that best matches their additional assumptions on the source model. We propose a novel blind source separation method based on probabilistic model of dynamic image sequences assuming each source dynamics as convolution of an input function and a source specific kernel (modeling organ impulse response or retention function). These assumptions are formalized as a Bayesian model with hierarchical prior and solved by the Variational Bayes method. The proposed prior distribution assigns higher probability to sparse source images and sparse convolution kernels. We show that the results of separation are relevant to selected tasks of dynamic renal scintigraphy. Accuracy of tissue separation with simulated and clinical data provided by the proposed method outperformed accuracy of previously developed methods measured by the mean square and mean absolute errors of estimation of simulated sources and the sources separated by an expert physician. MATLAB implementation of the algorithm is available for download.

Sparseness-Controlled Proportionate Affine Projection Sign Algorithms for Acoustic Echo Cancellation

Abstract: In order to reduce the steady-state misalignment of real-coefficient proportionate affine projection sign algorithm (RP-APSA) for sparse impulse responses, a memory RP-APSA is proposed in this paper by exploiting the historical values of proportionate factors, called MP-APSA. Further, to improve the robustness of MP-APSA and the recently proposed memory-improved RP-APSA (MIP-APSA) for impulse responses with mutative sparseness, two sparseness-controlled algorithms (SC-MP-APSA and SC-MIP-APSA) are developed by estimating the sparseness of the impulse response at each iteration. Simulation results in the context of acoustic echo cancellation show that the proposed algorithms retain good robustness against impulsive interferences. More importantly, the proposed sparseness-controlled algorithms can not only obtain low steady-state misalignment in both the sparse and dispersive situations, but also adapt to the variations in the sparseness of the impulse response.

SoQr: sonically quantifying the content level inside containers

Abstract: In this paper, we present SoQr, a sensor that can be attached to an external surface of a household item to estimate the amount of content inside it. The sensor consists of a speaker and a microphone. It outputs a short duration sine wave probing sound to excite a container and its content, and then records the container's impulse response. SoQr then extracts Mean Mel-Frequency Cepstral Coefficients from impulse response recordings of a container with different content levels and learns a support vector machine classifier. Results from a 10-fold cross validation of the prediction models on 19 common household items demonstrate that SoQr can correctly estimate the content level for these products with an average overall F-Measure above 0.96. We then further evaluated SoQr's robustness in different usage scenarios to gain an understanding of how the system performs and specific challenges that might arise when users interact with these products and the sensor.

A convex approach to blind deconvolution with diverse inputs

Abstract: This note considers the problem of blind identification of a linear, time-invariant (LTI) system when the input signals are unknown, but belong to sufficiently diverse, known subspaces. This problem can be recast as the recovery of a rank-1 matrix, and is effectively relaxed using a semidefinite program (SDP). We show that exact recovery of both the unknown impulse response, and the unknown inputs, occurs when the following conditions are met: (1) the impulse response function is spread in the Fourier domain, and (2) the N input vectors belong to generic, known subspaces of dimension K in ℝL. Recent results in the well-understood area of low-rank recovery from underdetermined linear measurements can be adapted to show that exact recovery occurs with high probablility (on the genericity of the subspaces) provided that K,L, and N obey the information-theoretic scalings, namely L ≳ K and N ≳ 1 up to log factors.

Transient response of a driver-pickup coil probe in transient eddy current testing

Abstract: Novel solutions that correctly incorporate all electromagnetic interactions arising in inductively coupled circuits are presented for the case of a coaxial driver and pickup coil probe encircling a long ferromagnetic conducting rod. The differential circuit equations are formulated in terms of the rod׳s impulse response using convolution theory, and solved by Fourier transform. The solutions presented here are the first to account for feedback between a ferromagnetic conductor and the driver and pickup coils, providing correct voltage response of the coils. Experimental results, obtained for the case of square wave excitation, are in excellent agreement with the analytical equations.

Optimal control of continuous systems with impulse controls

Abstract: Summary Impulse control problems, in which a continuously evolving state is modified by discrete control actions, have applications in epidemiology, medicine, and ecology. In this paper, we present a simple method for solving impulse control problems for systems of differential equations. In particular, we show how impulse control problems can be reformulated and solved as discrete optimal control problems. The method is illustrated with two examples. Published 2014. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Confidence Bands for Impulse Responses: Bonferroni vs. Wald†

Abstract: In impulse response analysis estimation uncertainty is typically displayed by constructing bands around estimated impulse response functions. If they are based on the joint asymptotic distribution possibly constructed with bootstrap methods in a frequentist framework, often individual confidence intervals are simply connected to obtain the bands. Such bands are known to be too narrow and have a joint coverage probability lower than the desired one. If instead the Wald statistic is used and the joint bootstrap distribution of the impulse response coefficient estimators is taken into account and mapped into the band, it is shown that such a band is typically rather conservative. It is argued that, by using the Bonferroni method, a band can often be obtained which is smaller than the Wald band.

Application of damage detection methods using passive reconstruction of impulse response functions.

Abstract: In structural health monitoring (SHM), using only the existing noise has long been an attractive goal. The advances in understanding cross-correlations in ambient noise in the past decade, as well as new understanding in damage indication and other advanced signal processing methods, have continued to drive new research into passive SHM systems. Because passive systems take advantage of the existing noise mechanisms in a structure, offshore wind turbines are a particularly attractive application due to the noise created from the various aerodynamic and wave loading conditions. Two damage detection methods using a passively reconstructed impulse response function, or Green's function, are presented. Damage detection is first studied using the reciprocity of the impulse response functions, where damage introduces new nonlinearities that break down the similarity in the causal and anticausal wave components. Damage detection and localization are then studied using a matched-field processing technique that aims to spatially locate sources that identify a change in the structure. Results from experiments conducted on an aluminium plate and wind turbine blade with simulated damage are also presented.

Front-end technologies for robust ASR in reverberant environments—spectral enhancement-based dereverberation and auditory modulation filterbank features

Abstract: This paper presents extended techniques aiming at the improvement of automatic speech recognition (ASR) in single-channel scenarios in the context of the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. The focus is laid on the development and analysis of ASR front-end technologies covering speech enhancement and feature extraction. Speech enhancement is performed using a joint noise reduction and dereverberation system in the spectral domain based on estimates of the noise and late reverberation power spectral densities (PSDs). To obtain reliable estimates of the PSDs—even in acoustic conditions with positive direct-to-reverberation energy ratios (DRRs)—we adopt the statistical model of the room impulse response explicitly incorporating DRRs, as well in combination with a novel proposed joint estimator for the reverberation time T 60 and the DRR. The feature extraction approach is inspired by processing strategies of the auditory system, where an amplitude modulation filterbank is applied to extract the temporal modulation information. These techniques were shown to improve the REVERB baseline in our previous work. Here, we investigate if similar improvements are obtained when using a state-of-the-art ASR framework, and to what extent the results depend on the specific architecture of the back-end. Apart from conventional Gaussian mixture model (GMM)-hidden Markov model (HMM) back-ends, we consider subspace GMM (SGMM)-HMMs as well as deep neural networks in a hybrid system. The speech enhancement algorithm is found to be helpful in almost all conditions, with the exception of deep learning systems in matched training-test conditions. The auditory feature type improves the baseline for all system architectures. The relative word error rate reduction achieved by combining our front-end techniques with current back-ends is 52.7% on average with the REVERB evaluation test set compared to our original REVERB result.