Fourier coefficients after gaps

Transformations of Fourier Coefficients

Introduction. In 1923 M. Fekete [2]2 introduced the concept of factor sequences that left invariant the class of a Fourier series. That is, Fekete investigated the conditions to which a sequence of constants (X„) must be subjected in order that (X„a„, X„i„) be Fourier coefficients of a function of the same class, K, as that of the function determined by (an, bn). Whenever (X„) has this property...

Edge Detection Using Fourier Coefficients

1. INTRODUCTION. Edge detection and the detection of discontinuities are important in many fields. In image processing, for example, one often needs to determine the boundaries of the items of which a picture is composed. (For more information about edge detection in image processing, see [10].) We consider the problem of detecting the edges present in a function when given the Fourier coeffici...

Quadrature formulas for Fourier coefficients

Auto-Tuning Using Fourier Coefficients

The Fourier transform is an integral part of signal processing of any kind. To be able to analyze an input signal as a superposition of infinitely many signals of different frequency allows for manipulation of the coefficients and, after completing the inverse Fourier transform, interesting effects on the initial signal. Thus, in my project I focused on creating pitch correction effects as seen...

Microphone Classification Using Fourier Coefficients

Media forensics tries to determine the originating device of a signal. We apply this paradigm to microphone forensics, determining the microphone model used to record a given audio sample. Our approach is to extract a Fourier coefficient histogram of near-silence segments of the recording as the feature vector and to use machine learning techniques for the classification. Our test goals are to ...