Digital Signal Processing 2: Filtering
Master digital filtering techniques, from basic concepts to adaptive processing. Learn filter design and implementation for signal analysis.
Master digital filtering techniques, from basic concepts to adaptive processing. Learn filter design and implementation for signal analysis.
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
Design and implement digital filters for signal processing applications
Analyze filter behavior in both time and frequency domains
Apply adaptive signal processing techniques to real-world problems
Understand and implement FIR and IIR filter designs
Master the z-transform and its applications in filter analysis
Skills you'll gain
This course includes:
4.95 Hours PreRecorded video
3 assignments
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FullTime access
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There are 3 modules in this course
This comprehensive course covers the fundamentals of Digital Signal Processing with a focus on filtering techniques. Students learn about digital filter design, implementation, and applications through hands-on examples and demonstrations. The curriculum progresses from basic filter concepts through advanced topics like adaptive signal processing, combining theoretical foundations with practical implementation skills. The course emphasizes both time-domain and frequency-domain analysis, making it essential for anyone interested in signal processing applications.
Digital Filters
Module 1 · 7 Hours to complete
Filter Design
Module 2 · 7 Hours to complete
Stochastic and Adaptive Signal Processing
Module 3 · 3 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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