Digital Signal Processing 1: Basic Concepts and Algorithms
- 4.6
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Course Summary
This course teaches the fundamentals of Digital Signal Processing and its applications in various fields such as audio processing, speech processing, and image processing.Key Learning Points
- Learn the basics of Digital Signal Processing
- Understand the applications of DSP in various fields such as audio processing, speech processing, and image processing
- Implement basic DSP algorithms using MATLAB
Related Topics for further study
Learning Outcomes
- Understand the basics of Digital Signal Processing
- Apply DSP algorithms in various fields such as audio processing, speech processing, and image processing
- Implement basic DSP algorithms using MATLAB
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of calculus and linear algebra
- Basic programming skills
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Assignments and quizzes
Similar Courses
- Digital Signal Processing
- Audio Signal Processing for Music Applications
- Speech Recognition Systems
Related Education Paths
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.
Knowledge
- The nature of discrete-time signals
- Discrete-time signals are vectors in a vector space
- Discrete-time signals can be analyzed in the frequency domain via the Fourier transform
Outline
- 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
Summary of User Reviews
This course on Digital Signal Processing is highly recommended by users, who have found it to be informative and practical. Many users praised the course for its clear and concise explanations of complex topics.Key Aspect Users Liked About This Course
Clear and concise explanations of complex topicsPros from User Reviews
- Well-structured course material
- Engaging and knowledgeable instructors
- Plenty of hands-on exercises and examples
- Useful real-world applications of DSP
Cons from User Reviews
- Some users found the course to be too basic
- Occasional technical difficulties with video lectures
- Lack of interaction with instructors and peers
Paolo Prandoni
4.6 Raiting
Discrete Time Signals and Systems, Part 1: Time Domain
- 0.0
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Enter the world of signal processing: analyze and extract meaning from the signals around us! Technological innovations have revolutionized the way we view and interact with the world around us....
Discrete Time Signals and Systems, Part 2: Frequency Domain
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Enter the world of signal processing: analyze and extract meaning from the signals around us! Technological innovations have revolutionized the way we view and interact with the world around us....
Signals and Systems, Part 2
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This course provides the basic toolkit for any signal processing application - the abstraction of signals and systems, from the point of view of analysis and characterization....
Digital Signal Processing 1: Basic Concepts and Algorithms
- 4.6
-
![]()
![]()
![]()
![]()
![]()
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....
