Dsp Software

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The following document describes the basic concepts of Digital Signal Processing (DSP) and also contains a variety of Recommended Reading links for more in-depth information.

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What is a DSP?

Digital Signal Processors (DSP) take real-world signals like voice, audio, video, temperature, pressure, or position that have been digitized and then mathematically manipulate them. A DSP is designed for performing mathematical functions like 'add', 'subtract', 'multiply' and 'divide' very quickly.

Signals need to be processed so that the information that they contain can be displayed, analyzed, or converted to another type of signal that may be of use. In the real-world, analog products detect signals such as sound, light, temperature or pressure and manipulate them. Converters such as an Analog-to-Digital converter then take the real-world signal and turn it into the digital format of 1's and 0's. From here, the DSP takes over by capturing the digitized information and processing it. It then feeds the digitized information back for use in the real world. It does this in one of two ways, either digitally or in an analog format by going through a Digital-to-Analog converter. All of this occurs at very high speeds.

To illustrate this concept, the diagram below shows how a DSP is used in an MP3 audio player. During the recording phase, analog audio is input through a receiver or other source. This analog signal is then converted to a digital signal by an analog-to-digital converter and passed to the DSP. The DSP performs the MP3 encoding and saves the file to memory. During the playback phase, the file is taken from memory, decoded by the DSP and then converted back to an analog signal through the digital-to-analog converter so it can be output through the speaker system. In a more complex example, the DSP would perform other functions such as volume control, equalization and user interface.

A DSP's information can be used by a computer to control such things as security, telephone, home theater systems, and video compression. Signals may be compressed so that they can be transmitted quickly and more efficiently from one place to another (e.g. teleconferencing can transmit speech and video via telephone lines). Signals may also be enhanced or manipulated to improve their quality or provide information that is not sensed by humans (e.g. echo cancellation for cell phones or computer-enhanced medical images). Although real-world signals can be processed in their analog form, processing signals digitally provides the advantages of high speed and accuracy.

Because it's programmable, a DSP can be used in a wide variety of applications. You can create your own software or use software provided by ADI and its third parties to design a DSP solution for an application. For more detailed information about the advantages of using DSP to process real-world signals, please read Part 1 of the article from Analog Dialogue titled: Why Use DSP? Digital Signal Processing 101- An Introductory Course in DSP System Design.

What's Inside a DSP?

A DSP contains these key components:

Program Memory: Stores the programs the DSP will use to process data
Data Memory: Stores the information to be processed
Compute Engine: Performs the math processing, accessing the program from the Program Memory and the data from the Data Memory
Input/Output: Serves a range of functions to connect to the outside world

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Analog Dialogue Series: Digital Signal Processing 101- An Introductory Course in DSP System Design

Part 1: Why use DSP? DSP Architecture and DSP Advantages Over Traditional Analog Circuitry
Part 2: Learn More About Digital Filters
Part 3: Implement Algorithms on a Hardware Platform
Part 4: Programming Considerations for Real-Time I/O

Let's Talk DSP: Commonly Used Words and What They Mean

DSP workshops are a very fast and efficient way to learn how to use Analog Devices DSP chips. The workshops are designed to develop a strong working knowledge of Analog Devices' DSP through lecture and hands-on exercises.

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1: The Breadth and Depth of DSP
2: Statistics, Probability and Noise
3: ADC and DAC
4: DSP Software
5: Linear Systems
6: Convolution
7: Properties of Convolution
8: The Discrete Fourier Transform
9: Applications of the DFT
10: Fourier Transform Properties
11: Fourier Transform Pairs
12: The Fast Fourier Transform
13: Continuous Signal Processing
14: Introduction to Digital Filters
15: Moving Average Filters
16: Windowed-Sinc Filters
17: Custom Filters
18: FFT Convolution
19: Recursive Filters
20: Chebyshev Filters
21: Filter Comparison
22: Audio Processing
23: Image Formation & Display
24: Linear Image Processing
25: Special Imaging Techniques
26: Neural Networks (and more!)
27: Data Compression
28: Digital Signal Processors
29: Getting Started with DSPs
30: Complex Numbers
31: The Complex Fourier Transform
32: The Laplace Transform
33: The z-Transform
34: Explaining Benford's Law

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