Digital-Signal-Processing-Coursera

My notes / works from Coursera courses.

Introduction

This repository contains my solutions for labs and programming assignments on Coursera courses. Certain resources required by the codes may be lacking due to limitations on downloading course materials from Coursera and uploading them to GitHub. The lacking resources are mostly datasets, pre-trained models or certain weight matrices.

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.

Courses

Digital Signal Processing 1: Basic Concepts and Algorithms

What I Learned
- The nature of discrete-time signals.
- Discrete-time signals can be analyzed in the frequency domain via the Fourier transform.
- Discrete-time signals are vectors in a vector space
Digital Signal Processing 2: Filtering

What I Learned
- Digital filters, how they work.
- Adaptive signal processing.
- Digital filter design.
Digital Signal Processing 3: Analog vs Digital

What I Learned
- The difference between continuous and discrete time.
- Quantization, A/D and D/A converters.
- Sampling and interpolation.
- Multirate signal processing.