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.
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À propos de ce cours
Ce que vous allez apprendre
Digital filters, how they work
Digital filter design
Adaptive signal processing
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École polytechnique fédérale de Lausanne
Programme du cours : ce que vous apprendrez dans ce cours
Module 2.1 Digital Filters
How digital filters work in time and in frequency.
Module 2.2: Filter Design
Learning how to choose and design the right filter using the z-transform and numerical tools.
Module 2.3: Stochastic and Adaptive Signal Processing
Analyzing and processing random signals and designing filters that adapt to unknown inputs.
Avis
Meilleurs avis pour DIGITAL SIGNAL PROCESSING 2: FILTERING
Excellent continuation to EPFL's DSP series. Be aware that the 3rd week requires a good foundation in statistics.
Add some numerical problems and solve them in your video lectures for the better understanding of concepts.
Best material on Signal Processing. Instructors emphasized on concepts. This course is enjoyable
This is a really good course that has very good exercises
À propos du Spécialisation Traitement numérique du signal
This Specialization provides a full course in Digital Signal Processing, with a focus on audio processing and data transmission. You will start from the basic concepts of discrete-time signals and proceed to learn how to analyze data via the Fourier transform, how to manipulate data via digital filters and how to convert analog signals into digital format. Finally, you will also discover how to implement real-time DSP algorithms on a general-purpose microcontroller. The solid theoretical bases provided by the four courses in this specialization are complemented by applied examples in Python, in the form of Jupyter Notebooks; exercises with solutions provide a wealth of examples in order to tackle the weekly homework.

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