Hardware and Software Solution Signal Compression Algorithms Based on the Chebyshev Polynomial

Abstract

In this paper, signal compression algorithm of Chebyshev polynomial is implemented. Upper case and lover case paralleled algorithm of compression signals processing suggested. However, audio and speech signal compressing accuracy in ADSPBF-561 dual core processor are analyzed. Index Terms—Discrete cosine transform, Chebyshev polynomial, signal processors, parallel programming, library automation programming. I. INTRODUCTION Today spectral methods of digital signal processing includes several transformations. Among them, Fourier transform, cosine and many similar transformations. These transformations differ each other with basis functions and the calculation processes. Using these transformations, first we calculate spectral values and based on them spectral values signal can be restored. After that using spectral value we can research internal properties of the signal. In these values are reflected analysis of signal, filtration process, signal compression processes. Processors and digital signal processors provides the process of calculation in digital signal processing. Digital signal processing is mainly implemented in real time systems. In the real time systems signals are received, processed and transmitted simultaneously. During signal processing in real-time systems, a signal incoming from the analog-to-digital converter using spectral methods obtains spectral values of signal (1). Using these spectral values we can organize of the signal processing. The one of the main direction of digital signal processing is the signal compression. Today, there are many methods of digital signal compression based on different mathematical methods. By using these digital compression algorithms in real time systems, we can solve kind of problems like saving memory space, time delay, signal processing time, performance of digital signal processors etc. Consequently, working on digital signal compressing and developing algorithms of digital signal compressing of the real time systems becomes important. coefficients compresses signal. These algorithms refers Newton, Lagrange, method of least squares and others. Using classic algorithms to digital signal compressing gives a good effect but it can't give normal results when you use in real time systems for digital signal processing. The main reason is increasing amount of signal values leads to increase number of solvable equation. Consequently, the solving the equations requires time, this leads to a delay which is not desirable for real time systems. Therefore, today many real time systems use digital signal processors. General structure of the process of direct and inverse transformation in a specialized signal processor is given in the illustration below (Fig. 1).