Digital Signal Processing 1: Basic Concepts and Algorithms
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Digital Signal Processing 1: Basic Concepts and Algorithms at Coursera Overview
Duration | 29 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
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Digital Signal Processing 1: Basic Concepts and Algorithms at Coursera Highlights
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Digital Signal Processing 1: Basic Concepts and Algorithms at Coursera Course details
- 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.
- In this series of four courses, you will 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.
Digital Signal Processing 1: Basic Concepts and Algorithms at Coursera Curriculum
Module 1.1: Digital Signal Processing: the Basics
1.1.1 What is digital signal processing?
1.1.2 Discrete-time signals
1.1.3.a How your PC plays discrete-time sounds
1.1.3.b The Karplus-Strong algorithm
1.1.4 Complex exponentials
Signal of the Day: Goethe's temperature measurement
Welcome to DSP One!
Introduction
Introduction
Introduction
Introduction
Practice homework
Notes and Supplementary Material
Homework for Module 1.1
Module 1.2: Signal Processing Meets Vector Space
1.2.1 Signal processing and vector spaces
1.2.2.a Vector space
1.2.2.b Signal spaces
1.2.3 Bases
1.2.4.a Subspace-based approximations
1.2.4.b Polynomial Approximation
Signal of the Day: Exoplanet hunting
Introduction
Introduction
Introduction
Introduction
Practice homework
Notes and Supplementary Material
Homework for Module 1.2
Module 1.3: Fourier Analysis: the Basics
1.3.1.a The frequency domain
1.3.1.b The DFT as a change of basis
1.3.2.a DFT definition
1.3.2.b Examples of DFT calculation
1.3.2.c Interpreting a DFT plot
1.3.3.a DFT analysis
1.3.3.b DFT example - analysis of musical instruments
1.3.3.c DFT synthesis
1.3.3.d DFT example - tide prediction in Venice
1.3.3.e DFT example - MP3 compression
1.3.4.a The short-time Fourier transform
1.3.4.b The spectrogram
1.3.4.c Time-frequency tiling
Signal of the Day: The first man-made signal from outer space
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
What have we learned?
Practice homework
Real-valued Transforms
Homework for Module 1.3
Module 1.4: Fourier Analysis: More Advanced Tools
1.4.1.a Discrete Fourier series
1.4.1.b Karplus-Strong revisited and DFS
1.4.2.a Karplus-Strong revisited and the DTFT
1.4.2.b Existence and properties of the DTFT
1.4.2.c The DTFT as a change of basis
1.4.3.a Sinusoidal modulation
1.4.3.b Tuning a guitar
1.4.4.a Relationships between transforms
1.4.4.b Zero padding
1.4.5 The fast Fourier transform
Signal of the Day: Tristan Chord
Introduction
What have we learned?
Introduction
What have we learned?
Introduction
Introduction
Introduction
Practice homework
Notes and Supplementary Material
Homework for Module 1.4
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