Digital Signal Processing 1: Basic Concepts and Algorithms
Master foundational DSP concepts: discrete-time signals, Fourier analysis, and signal processing algorithms.
Master foundational DSP concepts: discrete-time signals, Fourier analysis, and signal processing algorithms.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Digital Signal Processing Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Understand the nature of discrete-time signals and their properties
Master signal analysis using vector space concepts
Apply Fourier transform techniques for frequency domain analysis
Implement basic DSP algorithms using programming tools
Analyze real-world signal processing applications
Skills you'll gain
This course includes:
6.1 Hours PreRecorded video
4 assignments
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There are 4 modules in this course
This comprehensive course introduces the fundamental concepts of Digital Signal Processing (DSP). Students learn about discrete-time signals, vector spaces, and Fourier analysis, establishing a strong foundation in DSP theory and practice. The curriculum covers essential topics including signal representation, frequency domain analysis, transform methods, and practical applications in modern digital systems. Through hands-on examples and Python-based demonstrations, students develop practical skills in implementing DSP algorithms and understanding their real-world applications in communications and multimedia processing.
Digital Signal Processing: the Basics
Module 1 · 7 Hours to complete
Signal Processing Meets Vector Space
Module 2 · 5 Hours to complete
Fourier Analysis: the Basics
Module 3 · 8 Hours to complete
Fourier Analysis: More Advanced Tools
Module 4 · 7 Hours to complete
Fee Structure
Instructors
Expert in Signal Processing and Lecturer at EPFL
Paolo Prandoni is a dedicated lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), where he has been teaching signal processing for over two decades. His passion for the field began during his studies at the University of Padua in Italy, and he furthered his education by obtaining a PhD from EPFL, complemented by a research stint at Berkeley. Throughout his career, Paolo has balanced his academic pursuits with industry experience, working with various start-ups before founding Quividi in Paris, which focuses on innovative solutions in the realm of digital signal processing.At EPFL, Paolo teaches a comprehensive series of courses on digital signal processing, covering fundamental concepts, filtering techniques, and practical applications. His commitment to education and research has made him a prominent figure in the field, where he continues to inspire students and professionals alike to explore the vast possibilities of signal processing technology.
Renowned Signal Processing Expert and Professor at EPFL
Martin Vetterli is a distinguished professor at the École Polytechnique Fédérale de Lausanne (EPFL), where he has made significant contributions to the field of signal processing. His academic journey includes education at ETH Zurich, Stanford University, and EPFL, and he has previously held faculty positions at Columbia University and the University of California, Berkeley. With a lifelong passion for signal processing, Martin has focused his research on wavelets and filter banks, which are integral to various media standards such as MP3, JPEG, and WiFi. His expertise in these areas has positioned him as a leading figure in the intersection of technology and media.At EPFL, Martin teaches a series of courses on digital signal processing that cover fundamental concepts, filtering techniques, and practical applications. His commitment to education is evident in his engaging teaching style and dedication to mentoring students. Through his work, Martin continues to inspire the next generation of engineers and researchers in the rapidly evolving field of signal processing.
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