A Guide to Digital Signal Processing (DSP)

Digital signal processing (DSP) represents an exciting area of computer science and a world of possibilities for engineers designing new embedded system products. DSP technology uses specially designed programs and algorithms to manipulate analog signals and produce a signal that is higher-quality, less prone to degradation or easier to transmit. 

In this blog post, we explore some of the technology behind digital signal processing. We'll look at typical components, the key differences between analog and digital signals and the most common use cases for DSP.

What is Digital Signal Processing (DSP)?

Digital signal processing, or DSP, is a powerful technology with applications in many areas of science, engineering, health care, and communications. DSP technology enables the processing and manipulation of sensory data obtained from a variety of real-world sources. Visual images, sound waves, and even seismic waves can all act as inputs for digital signal processing. 

The general function of a DSP is to measure, compress, or filter an analog signal. This typically requires the DSP to perform a large number of simple mathematical functions (addition, subtraction, multiplication, division, and the like) within a fixed or constrained time frame. To achieve this, companies like Texas Instruments have developed specialized microprocessor chips that are optimized for the task of digital signal processing.

Digital signal processing technology is used wherever there is a need to compress, measure, or filter audio or another type of signal. The development of DSP began in the late 1960s and early 1970s when digital computers were first made available to governments and the largest corporations, but not yet to the general public. At this time, the application of DSP technology was focused on the military and government sectors, in areas like radar and sonar, space and oil exploration, and medical imaging.  As personal computing became commonplace through the 1980s and onward, digital signal processing saw a wider range of commercial and consumer-focused applications. Mobile phones, movie special effects, and mp3 files all depend on DSP technology.

Components of Digital Signal Processing

A typical digital signal processing system follows a basic architecture that facilitates the digital conversion and manipulation of an analog signal. The first requirement for DSP is always a signal source - there must be a signal to filter, measure, or compress. The first step in processing the signal is to convert the analog signal into a digital signal using an analog-to-digital converter (ADC). An ADC converts an input analog voltage to a digital measurement of that voltage. 

Following the conversion of the signal to the digital format, the data can be passed through a DSP microprocessor chip where the signal may be filtered, compressed or otherwise manipulated according to application-specific requirements. Once the digital signal has been suitably modified, it may be converted back into analog format with the use of a digital-to-analog converter (DAC). The end result will be a new analog signal that represents a digital modification of the original input signal.

A digital signal processing chip contains four main components:

  • Program Memory - DSP chips contain two types of memory. The first type, the program memory, stores the programs and algorithms that the chip will use to process data. Programming for DSP chips varies significantly by application.
  • Data Memory -  The second type of memory used in DSP chips is known as data memory. This is where the chip stores the data it receives and that will be processed on the chip. Data is typically received as a digital signal that was previously converted from an analog signal.
  • Compute Engine - The compute engine is the central processing unit of the DSP chip. This is where the computational power for the chip lives and where the algorithms from program memory will be applied to process data.
  • Input/Output - A DSP chip may possess a number of different types of ports, including serial ports, timers, host ports, external ports, LINK ports, and other types. Ports allow the DSP to send and receive data transmission from other devices, such as ADC or DAC converters. A DSP may also be incorporated into a larger computer system by port connections.
Example of digital signal processing Audio that is recorded using a microphone is captured in an analog format. A professional sound engineer will manipulate the sound using DSP before incorporating it into a final mix that will be released to listeners.

Image courtesy of Unsplash

How is DSP Different from Analog Signals?

Now that we've shed some light on how digital signal processing works, you might be wondering about various applications of DSP and the real value of converting analog signals into a digital format. To address this question, we need to understand more about the definitions and differences between analog and digital.

An analog signal is a continuous signal whose time variable is analogous to some physical quantity that changes over time, such as tone, voltage, or pressure. An analog signal depicting changes in voltage over time might reflect an amplitude of +/- 120 V, with the signal expressing all values within that range. In contrast, a digital signal would represent the same voltage as a sequence of discrete values, often coded for computers using the binary number system. 

Analog and digital signals contain the same information, but formatted in different ways. Analog signals reflect the reality that we live in a world where we can see an infinite number of different colors, hear an infinite number of tones and even smell an infinite number of smells. We can convert these data into a digital format that expresses each color, smell or sound as a combination of ones and zeroes. Then, we can write programs that manipulate the data in different and useful ways with the help of digital signal processing. As a final step, we can convert the digitally manipulated data back out of computer language and into analog form where we can hear or see the results.

Why Use Digital Signal Processing?

To demonstrate the versatility and usefulness of DSP, we can briefly explore just a few of the many applications for digital signal processing technology.

DSP in Audio Processing

Digital signal processing technology plays a major role in processing audio signals that are produced for human consumption. These generally appear in two forms: music and speech.

The process of recording music depends on DSP to produce a final mix that is optimally pleasing to the human ear. In the recording studio, the various components of a track are recorded in analog and converted into a digital format where they can be manipulated for volume, tonality, and a range of other features. DSP can assist with filtering, signal addition and subtraction (adding new sounds or subtracting unwanted sounds), editing, and more.

DSP is used in computer-generated speech applications, which combine digital recording technology and vocal tract simulation to replicate human speech patterns using a computer.

DSP in Echo Location

Digital signal processing plays a significant role in the functioning of modern radar systems. DSP can be used to compress a pulsed radiofrequency, increasing the accuracy of distance determination for objects detected on radar. A DSP chip can also increase the effective range of radar systems by filtering noise, and it may allow the operator to transmit radio waves pulses of varying shapes and lengths, enabling pulse optimization on a per-case basis. 

Conclusion

Are you building an embedded product that uses digital signal processing to solve real-world problems? Total Phase is committed to supporting the most innovative engineers and product developers with product testing and debugging tools that help reduce development costs and time to market for your DSP product. 

Our I2C and SPI tools offer full insight and transparency into the functioning of your device, streamlining the process of investigating, isolating, and rectifying unexpected errors and software anomalies. 

Learn More