Extraction Of Spectral Peak Parameters Using A Short-Time Fourier Transform Modeling And No Sidelobe Windows.

A new method which improves the estimation of frequency, amplitude and phase of the partials of a sound is presented. It allows the reduction of the analysis-window size from four periods to two periods. It therefore gives better accuracy in parameter determination, and has proved to remain efficient at low signal-to-noise ratios. The basic idea consists of using a parametric modeling of the short-time Fourier Transform. The method alternately estimates the complex amplitudes and the frequencies starting from the result of the classical analysis method. It uses least-square procedure and a firstorder limited expansion of the model around previous estimations. This method lead us to design new windows which do not have any sidelobe in order to help the convergence. Finally an analysis algorithm which has been built according to the observed behavior of the method for various kinds of sound is presented. 1. INTRODUCTION The additive synthesis model represents a sound s(n) as a finite s..

Sound Signals Decomposition Using a High Resolution Matching Pursuit

Sound recordings include transients and sustained parts. Their analysis with a basis expansion is not rich enough to represent efficiently all such components. Pursuit algorithms choose the decomposition vectors depending upon the signal properties. The dictionary among which these vectors are selected is much larger than a basis. Matching Pursuit is fast to compute, but can provide coarse representations. Basis Pursuit gives a better representation but is very expensive in terms of calculation time. This paper develops a High Resolution Matching Pursuit : it is a fast, high time-resolution, time-frequency analysis algorithm, that makes it likely to be used for musical applications. 1 Introduction The complexity of structures encountered in sound signals requires the development of adaptive lowlevel representations in order to provide meaningful analysis. Usual time-frequency analysis methods, such as Wavelet [KM88] or Short Time Fourier Transform [RS78] [Moo78], perform a decompositi..