An effective technique in locating a source based on intersections of hyperbolic curves defined by the time differences of arrival of a signal received at a number of sensors is proposed. The approach is noniterative and gives an explicit solution. It is an approximate realization of the maximum-likelihood estimator and is shown to attain the Cramer-Rao lower bound near the small error region. Comparisons of performance with existing techniques of beamformer, spherical-interpolation, divide and conquer, and iterative Taylor-series methods are made. The proposed technique performs significantly better than spherical-interpolation, and has a higher noise threshold than divide and conquer before performance breaks away from the Cramer-Rao lower bound. It provides an explicit solution form that is not available in the beamforming and Taylor-series methods. Computational complexity is comparable to spherical-interpolation but substantially less than the Taylor-series method. >

https://www.source-localiz.ucoz.ru/A_simple_and_efficient_estimator_for_hyperbolic_lo.pdf

A simple and efficient estimator for hyperbolic location

Systems and methods for navigating hypermedia using multiple coordinated input/output device sets. Disclosed systems and methods allow a user and/or an author to control what resources are presented on which device sets (whether they are integrated or not), and provide for coordinating browsing activities to enable such a user interface to be employed across multiple independent systems. Disclosed systems and methods also support new and enriched aspects and applications of hypermedia browsing and related business activities.

Method and apparatus for browsing using multiple coordinated device sets

http://www.eecs.northwestern.edu/~peters/references/LocalizationTechniquesMaoAustralia.pdf

Wireless sensor network localization techniques

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Digital processing of speech signals

A method for recognizing an audio sample locates an audio file that most closely matches the audio sample from a database indexing a large set of original recordings. Each indexed audio file is represented in the database index by a set of landmark timepoints and associated fingerprints. Landmarks occur at reproducible locations within the file, while fingerprints represent features of the signal at or near the landmark timepoints. To perform recognition, landmarks and fingerprints are computed for the unknown sample and used to retrieve matching fingerprints from the database. For each file containing matching fingerprints, the landmarks are compared with landmarks of the sample at which the same fingerprints were computed. If a large number of corresponding landmarks are linearly related, i.e., if equivalent fingerprints of the sample and retrieved file have the same time evolution, then the file is identified with the sample. The method can be used for any type of sound or music, and is particularly effective for audio signals subject to linear and nonlinear distortion such as background noise, compression artifacts, or transmission dropouts. The sample can be identified in a time proportional to the logarithm of the number of entries in the database; given sufficient computational power, recognition can be performed in nearly real time as the sound is being sampled.

System and methods for recognizing sound and music signals in high noise and distortion

Three noniterative techniques are presented for localizing a single source given a set of noisy range-difference measurements. The localization formulas are derived from linear least-squares "equation error" minimization, and in one case the maximum likelihood bearing estimate is approached. Geometric interpretations of the equation error norms minimized by the three methods are given, and the statistical performances of the three methods are compared via computer simulation.

Closed-form least-squares source location estimation from range-difference measurements

/pdf/spectral-modeling-synthesis-a-sound-analysis-synthesis-based-3pc4hwc8yc.pdf

Spectral modeling synthesis: A sound analysis/synthesis based on a deterministic plus stochastic decomposition

Historically, physical models have led to prohibitively expensive synthesis algorithms, and commercially available synthesizers do not yet appear to make use of them. These days, most synthesizers use either processed digital recordings (\sampling synthesis") or an abstract algorithm such as Frequency Modulation (FM). However, as computers become faster and cheaper, and as algorithms based on physical models become more e cient, we may expect to hear more from them.

Physical Modeling Using Digital Waveguides

Use of a bilinear conformal map to achieve a frequency warping nearly identical to that of the Bark frequency scale is described Because the map takes the unit circle to itself, its form is that of the transfer function of a first-order allpass filter Since it is a first-order map, it preserves the model order of rational systems, making it a valuable frequency warping technique for use in audio filter design A closed-form weighted-equation-error method is derived that computes the optimal mapping coefficient as a function of sampling rate, and the solution is shown to be generally indistinguishable from the optimal least-squares solution The optimal Chebyshev mapping is also found to be essentially identical to the optimal least-squares solution The expression 08517[arctan(006583fs)]/sup 1/2/-0916 is shown to accurately approximate the optimal allpass coefficient as a function of sampling rate f/sub s/ in kHz for sampling rates greater than 1 kHz A filter design example is included that illustrates improvements due to carrying out the design over a Bark scale Corresponding results are also given and compared for approximating the related "equivalent rectangular bandwidth (ERB) scale" of Moore and Glasberg (ACTA Acustica, vo82, p335-45, 1996) using a first-order allpass transformation Due to the higher frequency resolution called for by the ERB scale, particularly at low frequencies, the first-order conformal map is less able to follow the desired mapping, and the error is two to three times greater than the Bark-scale case, depending on the sampling rate

/pdf/bark-and-erb-bilinear-transforms-392mjxorqz.pdf

Julius O. Smith

Papers

System and methods for recognizing sound and music signals in high noise and distortion

Closed-form least-squares source location estimation from range-difference measurements

Spectral modeling synthesis: A sound analysis/synthesis based on a deterministic plus stochastic decomposition

Physical Modeling Using Digital Waveguides

Bark and ERB bilinear transforms