Digital Signal Processing

Nearly everything above the quantum level is an analog signal, which means that it can take an infinite number of values with no theoretical limit to the significant digits of that value. But there are signals that come close enough to being a discrete signal, or more particularly, a digital signal with 1 or 0 values. And we can nearly always take an analog signal and create a digital counterpart. So, why would we want to convert an analog signal to a digital signal and go through the work of processing the signal digitally? Because, many times, digital processing is much more effective and efficient than analog signal processing.

In this chapter, we will learn more about the difference between analog and digital signals, more about how the conversions are made from one to the other, the different processing techniques and the similarities and differences between the analog and discrete processing. In particular, discrete Fourier Transforms and Fast Fourier Transforms (or FFTs) are discussed and how the creation of the FFT has been instrumental in modern digital communication and music systems.

Instrumental in viewing and understanding digital signals, a spectrogram is a good way to visually understand a signal from a frequency and time perspective. With time as the x-axis and frequency as the y-axis, color represents the intensity of the signal of each frequency at each point in time. The way that the time domain signal is analyzed for its frequency domain intensity, or framing, is discussed as well as different framing options and their benefits.

 

This textbook is open source. Download for free at http://cnx.org/contents/778e36af-4c21-4ef7-9c02-dae860eb7d14@9.72.

 
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