A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

This paper makes available a concise review of data windows and their affect on the detection of harmonic signals in the presence of broad-band noise, and in the presence of nearby strong harmonic interference. We also call attention to a number of common errors in the application of windows when used with the fast Fourier transform. This paper includes a comprehensive catalog of data windows along with their significant performance parameters from which the different windows can be compared. Finally, an example demonstrates the use and value of windows to resolve closely spaced harmonic signals characterized by large differences in amplitude.

On the use of windows for harmonic analysis with the discrete Fourier transform

A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. ________________________________________ 1)International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: 510-643-7684. E-mail: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn-Ring 6, D-81739 Muenchen, Germany. Fax: 01149-636-44577, Email:rainer.storn@zfe.siemens.de. 2)836 Owl Circle, Vacaville, CA 95687, kprice@solano.community.net.

https://cis.poly.edu/~mleung/CS909/s04/Storn95-012.pdf

Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces

A software formant synthesizer is described that can generate synthetic speech using a laboratory digital computer. A flexible synthesizer configuration permits the synthesis of sonorants by either a cascade or parallel connection of digital resonators, but frication spectra must be synthesized by a set of resonators connected in parallel. A control program lets the user specify variable control parameter data, such as formant frequencies as a function of time, as a sequence of 〈time, value〉 points. The synthesizer design is described and motivated in Secs. I–III, and fortran listings for the synthesizer and control program are provided in an appendix. Computer requirements and necessary support software are described in Sec. IV. Strategies for the imitation of any speech utterance are described in Sec. V, and suggested values of control parameters for the synthesis of many English sounds are presented in tabular form.

Software for a cascade/parallel formant synthesizer

We use the concept of phase synchronization for the analysis of noisy nonstationary bivariate data. Phase synchronization is understood in a statistical sense as an existence of preferred values of the phase difference, and two techniques are proposed for a reliable detection of synchronous epochs. These methods are applied to magnetoencephalograms and records of muscle activity of a Parkinsonian patient. We reveal that the temporal evolution of the peripheral tremor rhythms directly reflects the time course of the synchronization of abnormal activity between cortical motor areas.

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Detection of n:m Phase Locking from Noisy Data: Application to Magnetoencephalography

sprightly style and is interesting from cover to cover. The comments, critiques, and summaries that accompany the chapters are very helpful in crystalizing the ideas and answering questions that may arise, particularly to the self-learner. The transparency in the presentation of the material in the book equips the reader to proceed quickly to a wealth of problems included at the end of each chapter. These problems ranging from elementary to research-level are very valuable in that a solid working knowledge of the invariant imbedding techniques is acquired as well as good insight in attacking problems in various applied areas. Furthermore, a useful selection of references is given at the end of each chapter. This book may not appeal to those mathematicians who are interested primarily in the sophistication of mathematical theory, because the authors have deliberately avoided all pseudo-sophistication in attaining transparency of exposition. Precisely for the same reason the majority of the intended readers who are applications-oriented and are eager to use the techniques quickly in their own fields will welcome and appreciate the efforts put into writing this book. From a purely mathematical point of view, some of the invariant imbedding results may be considered to be generalizations of the classical theory of first-order partial differential equations, and a part of the analysis of invariant imbedding is still at a somewhat heuristic stage despite successes in many computational applications. However, those who are concerned with mathematical rigor will find opportunities to explore the foundations of the invariant imbedding method. In conclusion, let me quote the following: "What is the best method to obtain the solution to a problem'? The answer is, any way that works." (Richard P. Feyman, Engineering and Science, March 1965, Vol. XXVIII, no. 6, p. 9.) In this well-written book, Bellman and Wing have indeed accomplished the task of introducing the simplicity of the invariant imbedding method to tackle various problems of interest to engineers, physicists, applied mathematicians, and numerical analysts.

Theory and application of digital signal processing

When Speech and Audio Signal Processing published in 1999, it stood out from its competition in its breadth of coverage and its accessible, intutiont-based style. This book was aimed at individual students and engineers excited about the broad span of audio processing and curious to understand the available techniques. Since then, with the advent of the iPod in 2001, the field of digital audio and music has exploded, leading to a much greater interest in the technical aspects of audio processing.This Second Edition will update and revise the original book to augment it with new material describing both the enabling technologies of digital music distribution (most significantly the MP3) and a range of exciting new research areas in automatic music content processing (such as automatic transcription, music similarity, etc.) that have emerged in the past five years, driven by the digital music revolution.New chapter topics include:Psychoacoustic Audio Coding, describing MP3 and related audio coding schemes based on psychoacoustic masking of quantization noiseMusic Transcription, including automatically deriving notes, beats, and chords from music signals.Music Information Retrieval, primarily focusing on audio-based genre classification, artist/style identification, and similarity estimation.Audio Source Separation, including multi-microphone beamforming, blind source separation, and the perception-inspired techniques usually referred to as Computational Auditory Scene Analysis (CASA).

Speech and Audio Signal Processing: Processing and Perception of Speech and Music

A computational algorithm for estimating pitch periods of speech in the time domain is presented, and two recent modifications of the algorithm are discussed in detail. The algorithm and its modifications have been found to be relatively accurate and efficient in tests on real and synthetic speech.

Parallel Processing Techniques for Estimating Pitch Periods of Speech in the Time Domain

A direct design procedure for nonrecursive digital filters, based primarily on the frequency-response characteristic of the desired filters, is presented. An optimization technique is used to minimize the maximum deviation of the synthesized filter from the ideal filter over some frequence range. Using this frequency-sampling technique, a wide variety of low-pass and bandpass filters have been designed, as well as several wide-band differentiators. Some experimental results on truncation of the filter coefficients are also presented. A brief discussion of the technique of nonuniform sampling is also included.

An approach to the approximation problem for nonrecursive digital filters

We introduce an approach to the design of low-pass (and, by extension, bandpass) digital filters containing only zeros. This approach is that of directly searching for transition values of the sampled frequency response function to reduce the sidelobe level of the response. It is shown that the problem is a linear program and a search algorithm is derived which makes it easier to obtain the experimental results.

Ben Gold

Papers

Theory and application of digital signal processing

Speech and Audio Signal Processing: Processing and Perception of Speech and Music

Parallel Processing Techniques for Estimating Pitch Periods of Speech in the Time Domain

An approach to the approximation problem for nonrecursive digital filters

A direct search procedure for designing finite duration impulse response filters