- Digital Signal Processing Tutorial
- DSP - Home
- DSP - Signals-Definition
- DSP - Basic CT Signals
- DSP - Basic DT Signals
- DSP - Classification of CT Signals
- DSP - Classification of DT Signals
- DSP - Miscellaneous Signals
- Operations on Signals
- Operations Signals - Shifting
- Operations Signals - Scaling
- Operations Signals - Reversal
- Operations Signals - Differentiation
- Operations Signals - Integration
- Operations Signals - Convolution
- Basic System Properties
- DSP - Static Systems
- DSP - Dynamic Systems
- DSP - Causal Systems
- DSP - Non-Causal Systems
- DSP - Anti-Causal Systems
- DSP - Linear Systems
- DSP - Non-Linear Systems
- DSP - Time-Invariant Systems
- DSP - Time-Variant Systems
- DSP - Stable Systems
- DSP - Unstable Systems
- DSP - Solved Examples
- Z-Transform
- Z-Transform - Introduction
- Z-Transform - Properties
- Z-Transform - Existence
- Z-Transform - Inverse
- Z-Transform - Solved Examples
- Discrete Fourier Transform
- DFT - Introduction
- DFT - Time Frequency Transform
- DTF - Circular Convolution
- DFT - Linear Filtering
- DFT - Sectional Convolution
- DFT - Discrete Cosine Transform
- DFT - Solved Examples
- Fast Fourier Transform
- DSP - Fast Fourier Transform
- DSP - In-Place Computation
- DSP - Computer Aided Design
- Digital Signal Processing Resources
- DSP - Quick Guide
- DSP - Useful Resources
- DSP - Discussion
Digital Signal Processing Tutorial
Digital Signal Processing (DSP) is an important branch of Electronics and Telecommunication Engineering that deals with the improvisation of reliability and accuracy of the digital communication by employing multiple techniques. This tutorial is meant for explaining the basic concepts of digital signal processing in a simple and easy-to-understand manner.
Digital signal processing is a technique that deals with the signal phenomenon. Along with basics of digital signal processing, in this tutorial, we have also shown the filter design using the concept of DSP. This tutorial is designed in a manner to have a good balance between theory and mathematical rigor.
What is Digital Signal Processing?
Digital signal processing, also referred to as DSP, is an engineering technique of converting real-world signals into digital form, analyzing them using algorithms, improve and manipulate them.
DSP is the core technology behind the information communication in modern devices like smartphones, laptops, remote sensing systems, medical devices, and much more.
A common example of digital signal processing that you experience in your daily life is using a noise-cancelling headphone or streaming a high-definition video on a social media platform.
Importance of Digital Signal Processing
Digital signal processing (DSP) is a crucial technology in the field of internet and communication. It is because it allows us to process real-world signals with great precision to use in various applications across different fields. The following applications highlights the importance of digital signal processing in our life −
- Processing of audio and voice signals
- Audio and video signal compression
- Noise reduction
- Processing of images like JPEG, PNG, etc.
- Telecommunication signal processing such as modulation, error correction, etc.
- Analysis of biomedical signals like ECG, EEG, etc.
All these operations are possible just because of digital signal processing.
Components of Digital Signal Processing
Take a look at the block diagram of a typical digital signal processing (DSP) system −
A typical digital signal processing system consists of the following key components −
- Input/Output − Connects the outside world with the digital signal processing system.
- Compute Engine − It is the core element of a DSP system and performs functions like mathematical processing, program access and execution, data access and processing.
- Program Memory − Memory component that stores programs required for digital signal processing.
- Data Memory − Memory component that stores input data and processed information.
Applications of Digital Signal Processing
Listed below are some of the common applications of digital signal processing (DSP) −
- Processing of audio, video, voice, or images
- Analysis of medical signals
- Pattern recognition
- Real-time analysis of real-world signals
- Improving data or signal quality
- Processing of radar and sonar signals, etc.
Important Terms Related to Digital Signals Processing
This section defines all the important terms related to Digital Signal Processing (DSP) that will be very helpful in grasping the advanced concepts in DSP −
Signal
An electrical quantity like current, voltage, or electromagnetic wave which is used for conveying information from one point to another is referred to as a signal. There are two types of signals namely, analog signals (also called continuous-time signals) and digital signals (also called discrete-time signals).
Digital Signal
A digital signal is one that represents information in the form of a sequence of discrete values. Digital signals are the backbone of digital signal processing (DSP).
Sampling
The process of converting real-world analog signals into digital form by taking samples of their amplitude at regular intervals is known as sampling. The intervals at which samples are taken are called sampling rate.
Quantization
Quantization is an important step in analog to digital conversion. It is defined as the process of mapping and approximating a large set of infinite sampled values of amplitude to a smaller set of discrete finite values.
Fourier Transform
Fourier Transform (FT) is a mathematical technique for converting time-domain signals into frequency-domain representation. It is used in DSP to perform operations like filtering, compression, and signal analysis.
Discrete Fourier Transform (DFT)
Discrete Fourier Transform (DFT) is a mathematical technique for converting a discrete sequence from time-domain to its equivalent frequency domain. It is used in DSP for spectral analysis, numerical analysis, filtering, etc.
Fast Fourier Transform (FFT)
Fast Fourier Transform (FFT) is nothing but computation of discrete Fourier Transform in an algorithmic format, where the computational part will be reduced. In other words, FFT is a mathematical tool for calculating Discrete Fourier Transform of a digital signal by using a computer with reduced number of calculations.
Convolution
Convolution is an important mathematical operation widely used in digital signal processing. It is used for combining two functions to produce a third function. Convolution is defined as the integral of the product of the two functions. It is used in DSP for filtering, image processing or audio processing.
Z-Transform
Z-Transform is a mathematical tool used for converting discrete-time signals into complex-valued frequency domain signal. It allows to analyze the discrete-time signals in the frequency domain. This transformation theory is mainly used to study system stability and design digital filters.
FIR Filter (Finite Impulse Response)
FIR filter is a type of digital filter that uses a finite set of input values and produces a limited duration impulse response. It is used as a fundamental component in many digital signal processing applications.
IIR Filter (Infinite Impulse Response)
IIR filter is also a type of digital filter. It is a feedback-based filter, hence it produces an impulse response by using past inputs and past outputs. It is widely used for efficient filtering in DSP applications.
Nyquist Theorem
Nyquist Theorem is one of the important principles in digital signal processing and it defines the essential condition for accurate analog to digital conversion. According this theorem, the sampling rate must be at least twice (double) of the bandwidth of the signal. It is essential for avoiding aliasing, a type of distortion.
Aliasing
Aliasing is a type of distortion in reconstructed signals that occurs when a signal is sampled at a rate lower than the required Nyquist rate. It degrades the quality of audio, video, or communication signals.
Impulse Response
Impulse response is defined as the output produced by a system when an impulse signal is applied to it as the input. An impulse signal is a type of signal whose amplitude is 1 at t = 0 and 0 at any other time.
Time-Invariant Systems
A time invariant system is a type of system whose response does not change with time. Thus, the response of a time invariant system to an input is independent of the time.
Stability
Stability is a property of a system which states that the output of the system remains bounded for a given bounded input. Thus, a stable system satisfies the condition of BIBO (Bounded Input and Bounded Output).
Prerequisites to Learn Digital Signal Processing
This tutorial on Digital Signal Processing (DSP) is designed for absolute beginners and does not require any prior knowledge or experience with DSP.
However, it will definitely help you in grasping the concepts better if you already have a basic understanding of foundational mathematical and engineering concepts such as calculus, linear algebra, Fourier Transformer, Z transform, and Signals and Systems.
Who Should Learn Digital Signal Processing?
This tutorial is meant for the undergraduate college students of electronics and telecommunication engineering, electrical engineering and computer science engineering.
In addition, it will also be useful for any enthusiastic reader who would like to understand more about various signals, systems, and the methods to process a digital signal.
FAQs on Digital Signals Processing
In this section, we have collected a set of some of the most Frequently Asked Questions (FAQs) on digital signal processing, followed by their answers −
Digital signal processing is a technology used for processing and manipulating digital signals. It is also referred as DSP in short. DSP is a collection of various mathematical operations like addition, subtraction, multiplication, division, scaling, shifting, etc. which are performed using programmable devices.
Digital signal processing is widely used in various applications across different fields. Some of the common applications of DSP are listed below −
- DSP is used for processing audio and voice signals like audio signal compression, speech recognition, format conversion, etc.
- DSP is used for processing digital images and video signals like image and video compression, editing, etc.
- In communication, digital signal processing is used for data transmission, radar and sonar signals processing.
- DSP is also used in biomedicals to process medical images and data of ECG or EEG.
- Consumer electronics such as smartphones, sound players, smart TVs, etc. uses digital signal processing to provide desired results.
Digital signal processing (DSP) is broadly classified into two types namely, Fixed-Point DSP and Floating-Point DSP.
- Fixed Point DSP is one that operates on integers only. In this type of DSP, at least 16 bits are used to represent data and this provides up to 65,536 possible values.
- Floating Point DSP can operate on rational numbers and it uses 32-bit representation of data. Thus, it can represent 4,294,967,296 possible values. In this DSP technique, the number of digitals before and after the decimal point can float based on the number size.
In DSP, a filter is an electronic device that removes the unwanted components from a signal. In digital signal processing, filters are used for various purposes like −
- Separating different signals
- Removing noise from signals
- Restoring distorted signals, etc.
The working steps of digital signal processing are explained here −
- Firstly, the digital signal processing takes input signals from external world, like audio, voice, image, video, etc.
- Then, it converts the real-world continuous signal into their digital representation.
- After that it performs analysis and processing of digitalized signals through mathematical functions like addition, subtraction, etc.
- Finally, it produces the results in desired format.
In DSP (Digital Signal Processing), a signal is a discrete time function which is obtained through sampling and quantization of an analog signal. It is also as a digital signal.
The key benefits of using digital signal processing (DSP) are listed here −
- Information processing with reduced noise
- Have low interference and signal distortion
- Cost effecting and faster
- Flexible in terms of programmability
- Consume less memory and power to perform complex processing, etc.
A digital signal processing system is a computing device that uses mathematical functions and techniques to process and manipulate digitalized real-world signals for a variety of purposes.
The following are some key features of a digital signal processor −
- Fast processing of large amount of data
- Improved processing performance
- Have both internal and external memory
- Have built-in digital filters
- Capability of noise reduction
- Support various DSP techniques like image compression, video processing, object recognition, motion detection, and more.
- Support advanced mathematical tools like DFT, FFT, etc. for analysis and design of systems.
Correlation is a mathematical method of quantifying the similarity and relationship between two spatial- or time-dependent signals.