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Digital Signal Processing 2: Filtering

Approx. 18 hours to complete

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Course Summary

This course covers advanced topics in Digital Signal Processing including spectral analysis, filter design, and signal processing applications. It is designed for students who have completed DSP1 or have equivalent knowledge.

Key Learning Points

Learn advanced digital signal processing techniques
Design and implement digital filters
Analyze signals in the frequency domain
Explore various signal processing applications

Learning Outcomes

Design and implement digital filters
Analyze signals in the frequency domain
Explore various signal processing applications

Prerequisites or good to have knowledge before taking this course

Completion of DSP1 or equivalent knowledge
Knowledge of basic calculus and linear algebra

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

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Notable People in This Field

Richard Lyons
Alan V. Oppenheim
Steven W. Smith

Related Books

Description

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices.

The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.

Knowledge

Digital filters, how they work
Digital filter design
Adaptive signal processing

Outline

Module 2.1 Digital Filters
2.1.1.a Linear time-invariant filters
2.1.1.b Convolution
2.1.2.a The moving average filter
2.1.2.b The leaky integrator
2.1.3.a Filter classification in the time domain
2.1.3.b Filter stability
2.1.4.a The convolution theorem
2.1.4.b Examples of frequency response
2.1.5.a Filter classification in the frequency domain
2.1.5.b The ideal lowpass filter
2.1.5.c Ideal filters derived from the ideal lowpass filter
2.1.5.d Demodulation revisited
SOTD: Can one hear the shape of a room?
Welcome to DSP Two!
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
What have we learned?
Practice homework
Homework for Module 2.1

Module 2.2: Filter Design
2.2.1.a Impulse truncation (and the Gibbs phenomenon)
2.2.1.b The window method
2.2.1.c Frequency sampling
2.2.2.a The z-transform
2.2.2.b Region of convergence and stability
2.2.3 Intuitive IIR designs
2.2.4.a Filter specifications
2.2.4.b IIR design
2.2.4.c FIR design
2.2.4.d Fractional delay and Hilbert filter
2.2.5.a Implementation of digital filters
2.2.5.b Real-time processing
Signal of the Day: Image Resolution and Space Exploration
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
Practice homework
Notes and Supplementary Materials
Homework for Module 2.2

Module 2.3: Stochastic and Adaptive Signal Processing
2.3.1.a Random Variables
2.3.1.b Stochastic Processes
2.3.1.c Power Spectral Density
2.3.1.d Filtering Random Processes
2.3.2.a Optimal Least Squares
2.3.2.b LPC Speech Coding
2.3.2.c The LMS Filter
2.3.2.d Echo Cancellation
Introduction
What have we learned?
Introduction
What have we learned?
Practice Homework
Notes and Supplementary Material

Summary of User Reviews

Learn advanced digital signal processing techniques in this comprehensive course on Coursera. Users have praised the course for its interactive content and practical approach towards learning.

Key Aspect Users Liked About This Course

Users have praised the practical approach towards learning in this course, which helps them apply the concepts in real-life scenarios.

Pros from User Reviews

Interactive content that engages users
Practical approach towards learning helps in real-life scenarios
Well-structured course content that covers advanced concepts
Great instructor who explains complex concepts in an easy-to-understand way

Cons from User Reviews

Some users found the course content to be too technical and difficult to follow
The pace of the course may be too slow for some users
Limited opportunities for hands-on practice with the concepts

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