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.
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.
Week 1
Introduction to the notation and basics of Digital Signal Processing
Learning Objectives
- Learn what is a signal, both in continous and in discrete time
Week 2
Modeling signals as vectors in an appropriate vector space. Using linear algebra to express signal manipulations.
Learning Objectives
- Review linear algebra and vector space theory, and learn how to model signals as elements of a vector space
Week 3
The fundamental concepts behind the Fourier transform and the frequency domain
Learning Objectives
- Learn the frequency representation of finite-length signals
- Understand how signals can be described either in the time domain or in the frequency domain
Week 4
Delving deeper in the world of Fourier analysis.
Learning Objectives
- Learn how to represent arbitrary signals in the frequency domain
- Advance your knowledge of Fourier analysis
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.
The goal, for students of this course, will be to 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.
Week 1
How digital filters work in time and in frequency.
Learning Objectives
- Give examples of simple filters
- Classify filters in the time and frequency domain
Week 2
Learning how to choose and design the right filter using the z-transform and numerical tools.
Week 3
Analyzing and processing random signals and designing filters that adapt to unknown inputs.
Learning Objectives
- Explain the difference between a deterministic and a random signal
- Design an adaptive signal processing system
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.
The goal, for students of this course, will be to 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.
Week 1
From continuous time to discrete time and vice versa.
Learning Objectives
- Compare continuous and discrete time
- Describe interpolation methods
- Demonstrate a sampling theorem and its applications
Week 2
What happens when we sample continuous-time signals and problems we should anticipate.
Learning Objectives
- Understand aliasing
- Judge the conditions for alias-free sampling
Week 3
How to change the sampling rate entirely from the discrete-time domain.
Week 4
Going from analog to digital, and vice-versa.
Learning Objectives
- Explain what a quantizer does
- Describe how to change the sampling rate of a signal
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.
The goal, for students of this course, will be to 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.
Week 1
Image processing and the JPEG compression standard
Learning Objectives
- Interpret the basic concepts of image processing
- Build simple image processing algorithms
- Discover the principles of image compression
Week 2
Digital communication systems: voiceband modems and ADSL
Week 3
Real-time audio signal processing on a Nucleo microcontroller
Learning Objectives
- Estimate the challenges of real-time algorithms on dedicated hardware
- Discover the tools used in hardware DSP
- Build a working system from off-the-shelf components
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