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Digital Filters - FIR & IIR

4.4 Filled star Filled star Filled star Filled star Empty star
Based on 73 ratings
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Sarita Bopalkar

This course covers Digital filters designing techniques with practical implementation in Python, for filters like Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). This course is suitable for students, engineers, academicians, illustrating theory as well as practical implementation with variety of examples.

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This course covers Digital filters designing techniques with practical implementation in Python, for filters like Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). This course is suitable for students, engineers, academicians, illustrating theory as well as practical implementation with variety of examples.

During my teaching experience, I noticed that students are more confused about the basic concepts of Filter design and struggle to design filters in the correct way. This course will give further confidence to students, engineers for filter designing in all aspects, theoretically and practically, and enable them to apply filters in different DSP applications

This course starts with basic filtering concept in Digital Signal Processing (DSP) and then explains how DT-LTI system works as filter. It also explains basic characteristics of DT-LTI system to work as FIR and IIR filter.

The subsequent sections explain various FIR filter design methods like windowing & frequency sampling and IIR filter design techniques in very easy way, step by step, with examples solved theoretically and practically. Here FIR filter and IIR filters are explained in detail along with applications.

This course will be useful for students taking the ‘DSP’ course as well as for engineers who would like to implement filters practically.

I will be regularly updating this course in near future.

Happy Learning.

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What's inside

Syllabus

Course Overview
Characteristics of DT-LTI system has explained in detail. It also explains concept of FIR and IIR filter and DT-LTI system as a filter
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The concept of linear phase characterises of DT-LTI has explained here. As these characteristics are very important to develop filters for the systems related to sound and many other systems for eg. Musical system, speech processing etc

Here you will understand Linearity and Time invariant property of DT- LTI system and importance of these properties. As most of real time systems like vocal system can be represented as DT-LTI system. Here you will also understand the relation between input, impulse response and output of DT-LTI system in time and frequency domain both.

In digital signal processing DT-LTI systems acts as a frequency selective filters i.e filtering various frequency components at it’s input. This nature of filtering action is determined by the frequency response characteristics H(w) i.e DT- LTI systems modifies the input signal spectrum X(w) according to it’s frequency response H(w). By proper selection of frequency response H(w) of DT-LTI system we can design Low pass filter, High pass filter etc.

Here you will understand the concept of filtering action and how to apply that concept in Digital signal processing

This lecture explains How DT-LTI can acts as FIR and IIR filter. Here your will understand whether to design FIR or IIR filter for particular application.

This quiz helps you to clear you basic undetstanding about DT-LTI system as a filter

Always we are interested to develop causal and stable system. And importance of FIR filter(DT-LTI system) is ‘It has linear phase characteristics’ . To achieve linear phase, causality and stability of DT-LTI system( FIR filter) what should be location of poles and zeroes of the system. This section gives you complete idea about location of poles and zeros of the DT-LTI system to develop causal, stable and linear phase characteristics

Always we are interested to design causal, linear phase and real valued filter so it is necessary to understand  zeros which satisfices this condition so that we can design FIR filter with these zeros only

Understand condition of Linear Phase Characteristics of FIR filter and four different types of FIR filter.

Understand why some application strictly prefer FIR filter due it’s Linear Phase characteristics

This gives you clear idea about the principle of windowing technique in designing FIR filter

These steps helps you to design FIR filter in systematic and easy way

Different examples gives complete idea about the designing FIR filter with windowing technique. These designing examples tries to cover different variations in the designing statement.

This code includes detail explain about FIR filter filtering action. here we will understand the first generate a signal, check which frequency components are exits in the signal. Then generate a FIR filter according to the application requirement and filter the input signal through it. 

Here same above code extended for different types of filter, different window functions

Gives you clear idea about designing techniques method used for IIR filter.

As digital IIR filter designed by analog designing method, to get digital filter transfer function H(z) transformation techniques are required to map from analog(S-domain) to digital (Z-domain). IIM is one of the transformation technique used. In this lecture you will understand the principle used in IIM.

Explains what is aliasing effect and how it occurs in IIM

Gives complete derivation of formula of poles of Butterworth filter

Gives complete explanation about transfer function of Butterworth filter. It starts with one pole then two pole, three poles Transfer function of filter and then we are developing generalised formula for transfer function of the Butterworth filter which can be directly used in filter designing and makes the designing easy.

Here generalized transfer function for IIR filter has explained. This generalized transfer function can be used for both IIR filter Butterworth and Chebyshev filter.

Helps to design filter in a systematic and easy way

Understand the designing statement and how to design step wise in systematic way

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides practical implementation in Python, which is valuable for students and engineers looking to apply digital filters in real-world scenarios and DSP applications
Explores FIR and IIR filter design techniques, which are fundamental concepts in digital signal processing and essential for various engineering applications
Covers windowing and frequency sampling methods for FIR filter design, offering a comprehensive understanding of different approaches to filter implementation
Includes a section on linear phase characteristics of DT-LTI systems, which is crucial for developing filters for sound-related systems like musical systems and speech processing
Requires familiarity with Python libraries for signal processing, which may necessitate additional learning for those without prior experience in Python programming
Focuses on Infinite Impulse Response filter design techniques, which are essential for students taking DSP courses and engineers implementing filters practically

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Reviews summary

Solid foundation in digital filter design

Learners say this course provides a solid foundation in Digital Signal Processing (DSP) filter design, covering FIR and IIR filters. Many found the explanations clear, balancing theory and practical implementation. The Python examples were highlighted as useful and well-explained. Some noted it can be challenging without strong DSP basics. It's considered a valuable resource for designing and implementing digital filters.
Design methods taught systematically.
"The step-by-step design examples were very helpful."
"The course flow made sense, building complexity gradually."
"I liked how each filter type was introduced methodically."
Good blend of concepts and application.
"Loved how it balanced the math with practical design steps."
"It's not just dry theory, but real-world implementation too."
"Helped me understand the 'why' and the 'how' of filter design."
Hands-on coding is a major strength.
"The Python implementations are gold. They really solidify the theory."
"I could immediately apply the Python examples to my work."
"Seeing the filters designed in code made everything click."
Complex concepts are explained clearly.
"The instructor explained the concepts in a very easy way."
"I finally understood how FIR and IIR filters work after this course."
"Everything was broken down step-by-step for clarity."
Requires prior DSP knowledge.
"If you are completely new to DSP, be prepared for a steep curve."
"I needed to review external resources for basic DSP concepts."
"It assumes some familiarity with signals and systems fundamentals."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Digital Filters - FIR & IIR with these activities:
Review Signals and Systems Fundamentals
Reinforce your understanding of fundamental concepts in signals and systems. A solid grasp of these concepts is crucial for understanding digital filter design.
Show steps
  • Review definitions of signals and systems.
  • Practice problems related to LTI systems.
  • Study the properties of signals in time and frequency domains.
Read 'Understanding Digital Signal Processing' by Steven W. Smith
Supplement your learning with a comprehensive textbook on digital signal processing. This book offers a deeper dive into the theory and applications of digital filters.
View Digital Signal Processing: A Practical Guide... on Amazon
Show steps
  • Read the chapters on filter design.
  • Work through the examples provided in the book.
  • Attempt the end-of-chapter problems.
Design FIR filters using Python
Sharpen your filter design skills through practical exercises. Repeated practice will solidify your understanding of the design process.
Show steps
  • Implement FIR filters using windowing techniques.
  • Design FIR filters using the frequency sampling method.
  • Compare the performance of different FIR filter designs.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Review 'Digital Signal Processing: Principles, Algorithms, and Applications' by Proakis and Manolakis
Deepen your understanding of the theoretical underpinnings of digital filters. This book provides a rigorous mathematical treatment of the subject.
View Digital Signal Processing on Amazon
Show steps
  • Study the chapters on FIR and IIR filter design.
  • Work through the mathematical derivations.
  • Solve the advanced problems at the end of each chapter.
Create a blog post on IIR filter design
Solidify your understanding of IIR filter design by explaining the concepts to others. Writing a blog post will force you to organize your thoughts and clarify any remaining doubts.
Show steps
  • Research different IIR filter design techniques.
  • Write a clear and concise explanation of the design process.
  • Include examples and diagrams to illustrate the concepts.
  • Publish your blog post online.
Develop a real-time audio filtering application
Apply your knowledge of digital filters to a practical project. Building a real-time application will expose you to the challenges of implementing digital filters in a real-world setting.
Show steps
  • Choose a suitable platform for your application.
  • Implement FIR and IIR filters in your application.
  • Test and evaluate the performance of your filters.
  • Optimize your application for real-time performance.
Contribute to an open-source DSP library
Enhance your skills by contributing to a real-world project. Working with an open-source library will expose you to best practices in software development and collaborative coding.
Show steps
  • Find an open-source DSP library on GitHub.
  • Identify a bug or feature that you can contribute.
  • Submit a pull request with your changes.
  • Respond to feedback from the maintainers.

Career center

Learners who complete Digital Filters - FIR & IIR will develop knowledge and skills that may be useful to these careers:
Signal Processing Engineer
A Signal Processing Engineer develops algorithms and systems to analyze, modify, and synthesize signals such as audio, images, and sensor data. This course on digital filters, including Finite Impulse Response and Infinite Impulse Response filter design, is highly relevant. A core task of a Signal Processing Engineer is the design and implementation of digital filters. Understanding the characteristics of Discrete Time Linear Time Invariant systems, as covered in the course, helps build a solid foundation for designing effective filters. The course provides practical implementation in Python, enabling engineers to apply filters in various Digital Signal Processing applications. Signal Processing Engineers can use the filter design techniques learned in this course to modify signals to remove noise or extract useful information.
See salaries and explore the career path for Signal Processing Engineer
Audio Engineer
An Audio Engineer specializes in recording, manipulating, mixing, and mastering audio signals. This course on Finite Impulse Response and Infinite Impulse Response digital filters is a great fit for Audio Engineers. Audio Engineers frequently use filters to shape the frequency content of audio signals, remove unwanted noise, and create special effects. The course explains linear phase characteristics of Discrete Time Linear Time Invariant systems, which are very important to develop filters for systems related to sound. Audio Engineers can benefit from the practical implementation examples in Python, allowing them to design and implement custom audio filters. Understanding the different types of Finite Impulse Response and Infinite Impulse Response filters, as taught in this course, helps an Audio Engineer make informed decisions.
See salaries and explore the career path for Audio Engineer
DSP Developer
A Digital Signal Processing Developer designs and implements algorithms for processing signals in various applications. This course on Finite Impulse Response and Infinite Impulse Response digital filters is ideally suited for any Digital Signal Processing Developer. Digital Signal Processing Developers are heavily involved in filter design and implementation. A DSP Developer can leverage the knowledge gained from this course to design and implement efficient and effective digital filters. The course emphasizes the practical implementation of filters in Python, which allows Digital Signal Processing Developers to quickly prototype and deploy algorithms. Finite Impulse Response and Infinite Impulse Response filters can be applied to many applications, including audio processing, image processing, and communications.
See salaries and explore the career path for DSP Developer
Acoustic Consultant
An Acoustic Consultant provides expert advice on sound and vibration issues, often involving noise control and acoustic design. This course on digital Finite Impulse Response and Infinite Impulse Response filters is relevant. Acoustic Consultants use filters to analyze sound signals, identify noise sources, and design effective noise mitigation strategies. An Acoustic Consultant can directly apply the filter design techniques taught in this course to solve real-world acoustic problems. The course's practical implementation examples in Python enable the development of custom acoustic analysis tools. The understanding of Discrete Time Linear Time Invariant systems and filter characteristics that this course provides is essential for any Acoustic Consultant.
See salaries and explore the career path for Acoustic Consultant
Image Processing Engineer
An Image Processing Engineer develops algorithms and systems to process, analyze, and enhance digital images. This course focusing on Finite Impulse Response and Infinite Impulse Response digital filter design is very useful. Image processing often involves filtering operations to sharpen images, reduce noise, or extract features. An Image Processing Engineer can apply the filter design techniques learned in this course to enhance images and extract relevant information. The course covers various filter design methods, including windowing and frequency sampling, which are directly applicable to image processing tasks. The practical implementation examples in Python enable Image Processing Engineers to implement and test different filtering algorithms.
See salaries and explore the career path for Image Processing Engineer
Embedded Systems Engineer
An Embedded Systems Engineer designs and develops software and hardware for embedded systems. This course covering Finite Impulse Response and Infinite Impulse Response digital filters may be relevant. Embedded systems often process signals from sensors, and digital filters are frequently used to remove noise and extract relevant information. An Embedded Systems Engineer can use the filter design techniques taught in this course to improve the performance of embedded systems. The course provides an understanding of Discrete Time Linear Time Invariant systems and their characteristics, which can help Embedded Systems Engineers design effective signal processing algorithms. The practical examples in Python enable Embedded Systems Engineers to implement and test filters.
See salaries and explore the career path for Embedded Systems Engineer
Telecommunications Engineer
A Telecommunications Engineer designs and maintains communication systems, including networks and equipment. This course covering Finite Impulse Response and Infinite Impulse Response digital filters can be useful. Digital filters are essential for signal processing in telecommunications, enabling noise reduction, signal equalization, and channel separation. A Telecommunications Engineer can apply the filter design techniques learned in this course to optimize communication system performance. The course's practical Python implementation examples provide a hands-on approach to implementing filters. Telecommunications Engineers will benefit from Discrete Time Linear Time Invariant concepts for designing robust communication systems.
See salaries and explore the career path for Telecommunications Engineer
Aerospace Engineer
An Aerospace Engineer designs and develops aircraft, spacecraft, and related systems. This course on Finite Impulse Response and Infinite Impulse Response digital filters is relevant for certain specializations. Aerospace systems often use digital filters to process sensor data from navigation systems, control systems, and communication systems. An Aerospace Engineer can leverage the filter design techniques taught in this course to improve the performance and reliability of aerospace systems. The course provides practical examples in Python. The course's deep-dive into Discrete Time Linear Time Invariant systems would be particularly beneficial to those working on avionics and control systems.
See salaries and explore the career path for Aerospace Engineer
Control Systems Engineer
A Control Systems Engineer designs and implements systems to control the behavior of dynamic systems. This course covering Finite Impulse Response and Infinite Impulse Response digital filters may be useful. Control systems often use digital filters to process sensor data and implement control algorithms. A Control Systems Engineer can use the filter design techniques taught in this course to improve the performance and stability of control systems. The course provides an understanding of Discrete Time Linear Time Invariant systems and their characteristics, which can help Control Systems Engineers design effective controllers. The practical examples provided in Python can help Control System Engineers implement filters in real-time control applications.
See salaries and explore the career path for Control Systems Engineer
Biomedical Engineer
A Biomedical Engineer applies engineering principles to solve problems in medicine and biology. This course focusing on Finite Impulse Response and Infinite Impulse Response digital filters may be useful. Biomedical signal processing, such as analyzing electrocardiogram or electroencephalogram signals, involves filtering to remove noise and artifacts. A Biomedical Engineer can use the filter design techniques taught in this course to improve the quality and accuracy of biomedical measurements. The practical implementation examples in Python make incorporating these filters into data analysis pipelines much easier. An understanding of Discrete Time Linear Time Invariant systems can benefit those developing medical devices.
See salaries and explore the career path for Biomedical Engineer
Data Scientist
A Data Scientist analyzes data to extract insights and build predictive models. This course on Finite Impulse Response and Infinite Impulse Response digital filters may be useful for Data Scientists. Digital filters can be used to preprocess data by removing noise or extracting relevant features. A Data Scientist may find the filter design techniques taught in this course useful for cleaning noisy datasets. The course's practical implementation examples in Python provide a foundation for implementing data preprocessing pipelines. Understanding the characteristics of Discrete Time Linear Time Invariant systems can also help Data Scientists build more accurate and robust models.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. This course about Finite Impulse Response and Infinite Impulse Response digital filters design can be relevant. Machine learning models often require preprocessed data, and digital filters can play a crucial role in cleaning and feature extraction. A Machine Learning Engineer can apply the filter design techniques learned in this course to improve the performance of machine learning models. The practical implementation examples in Python facilitate the integration of digital filters into machine learning pipelines. Understanding the fundamentals of Discrete Time Linear Time Invariant systems can offer valuable insights into signal processing for machine learning applications.
See salaries and explore the career path for Machine Learning Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and programs robots for various applications. This course on Finite Impulse Response and Infinite Impulse Response digital filters could be helpful for Robotics Engineers. Robots often rely on sensor data that can be noisy, and digital filters can be applied to clean and process this data, improving robot performance. A Robotics Engineer can use the filter design techniques taught in this course to enhance the accuracy of robot perception and control systems. The practical implementation examples in Python enable Robotics Engineers to integrate these filters into robot software. Understanding the basics of Discrete Time Linear Time Invariant systems assist them in optimizing robot control algorithms.
See salaries and explore the career path for Robotics Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. Digital filters are relevant to software engineering. Software Engineers working on applications involving audio or image processing might find this course on Finite Impulse Response and Infinite Impulse Response digital filters helpful. This course, with its practical implementation in Python, can provide a Software Engineer with the skills to implement filtering algorithms for various applications. Understanding the design and implementation of Discrete Time Linear Time Invariant systems can be beneficial. This course may be useful for Software Engineers wanting to broaden their skill set in digital signal processing.
See salaries and explore the career path for Software Engineer
Network Engineer
A Network Engineer designs, implements, and manages computer networks. This course focusing on Finite Impulse Response and Infinite Impulse Response digital filters may be useful. While not a core skill, an understanding of signal processing and filtering can be valuable in network performance analysis and optimization. A Network Engineer can apply the filter design techniques taught in this course to analyze network traffic patterns and identify anomalies. The practical implementation examples in Python make it easier to develop specialized tools for network monitoring. Understanding the basics of Discrete Time Linear Time Invariant systems can enhance a Network Engineer's ability to troubleshoot and improve network performance.
See salaries and explore the career path for Network Engineer

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Digital Filters - FIR & IIR.
Digital Signal Processing
Save
Comprehensive and rigorous treatment of digital signal processing. It covers a wide range of topics, including filter design, in great detail. It is commonly used as a textbook in graduate-level DSP courses. This book provides a strong theoretical foundation for understanding digital filters and their applications.
Digital Signal Processing
Paperback
Check
Digital Signal Processing
Kindle Edition
Check
Cover image
Digital Signal Processing: A Practical Guide for...
Save
Provides a practical and intuitive approach to digital signal processing. It covers the fundamentals of DSP, including filter design, in a clear and accessible manner. It is particularly useful for students who prefer a hands-on approach to learning. This book valuable resource for understanding the underlying principles of digital filters.
Digital Signal Processing: A Practical Guide for...
Paperback
$$$
Digital Signal Processing: A Practical Guide for...
Kindle Edition
$$$

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Course image
Rating
4.4 Filled star Filled star Filled star Filled star Empty star
Effort
10 total hours
Via
Udemy
Instructor
Sarita Bopalkar
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Provides practical implementation in Python, which is valuable for students and engineers looking to apply digital filters in real-world scenarios and DSP applications
Explores FIR and IIR filter design techniques, which are fundamental concepts in digital signal processing and essential for various engineering applications
Covers windowing and frequency sampling methods for FIR filter design, offering a comprehensive understanding of different approaches to filter implementation
Includes a section on linear phase characteristics of DT-LTI systems, which is crucial for developing filters for sound-related systems like musical systems and speech processing
Requires familiarity with Python libraries for signal processing, which may necessitate additional learning for those without prior experience in Python programming
Focuses on Infinite Impulse Response filter design techniques, which are essential for students taking DSP courses and engineers implementing filters practically
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