Digital Signal Processing

DSP in day-to-day life:
DSP technology is nowadays commonplace in such devices as mobile phones, multimedia computers, video recorders, CD players, hard disc drive controllers and modems, and has replaced analog circuitry in TV sets and telephones as well. An important application of DSP is in signal compression and decompression. Signal compression is used in digital cellular phones to allow a greater number of calls to be handled simultaneously within each local "cell". DSP signal compression technology allows people not only to talk to one another but also to see one another on their computer screens, using small video cameras mounted on the computer monitors, with only a conventional telephone line linking them together. In audio CD systems, DSP technology is used to perform complex error detection and correction on the raw data as it is read from the CD.

What is DSP?

Digital signal processing ('DSP') is the study of signals in a digital representation and the processing methods of these signals. DSP and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, image processing, signal processing for communications, biomedical signal processing, etc.
Since the goal of DSP is usually to measure or filter continuous real-world analog signals, the first step is usually to convert the signal from an analog to a digital form, by using an analog to digital converter. Often, the required output signal is another analog output signal, which requires a digital to analog converter.

Digital Signal Processors:

The algorithms required for DSP are sometimes performed using specialized computers, which make use of specialized microprocessors called digital signal processors by electronics manufacturers such as Texas Instruments, Analog Devices and Motorola. These process signals in real time and are generally purpose-designed application-specific integrated circuits (ASICs). When flexibility and rapid development are more important than unit costs at high volume, DSP algorithms may also be implemented using field-programmable gate arrays (FPGAs).

DSP Techniques and Algorithms:

Although some of the mathematical theory underlying DSP techniques, such as Fourier and Hilbert Transforms, digital filter design and signal compression, can be fairly complex, the numerical operations required actually to implement these techniques are very simple, consisting mainly of operations that could be done on a cheap four-function calculator. The architecture of a DSP chip is designed to carry out such operations incredibly fast, processing hundreds of millions of samples every second, to provide real-time performance: that is, the ability to process a signal "live" as it is sampled and then output the processed signal, for example to a loudspeaker or video display. All of the practical examples of DSP applications mentioned earlier, such as hard disc drives and mobile phones, demand real-time operation.


Fourier transform (in the form of FFT) is a very commonly used technique for analyzing and filtering digital signals. FFT is not very optimal in many filtering applications because it assumes that the frequency spectrum is not changing over time. There are other digital-signal-processing (DSP) techniques that are more advantageous for filtering a real-world signal. The infinite-impulse-response (IIR) and finite-impulse-response (FIR) filters can be implemented very inexpensively, and they work on continuous stream of data. The wavelet transform is worth investigating new technique for signal analyzing and filtering. Like the FFT, wavelet transform converts time-domain data into the frequency domain. The wavelet transform assumes that the frequency spectrum is changing over time. The result of a wavelet transform is a bit harder to read, but more meaningful for many applications.There are also many other relevant signal processing techniques targeted for different kind of applications.

Related Sites:

• Signal processing
• MATLAB DSP Signal Processing Toolbox
• Fourier transform
• Digital Signal Processors