Sparsity and Compression (Data-Driven Science and Engineering)
by Steve Brunton · YouTube
Lecture playlist by Steve Brunton accompanying the sparsity and compression chapter of the book Data-Driven Science and Engineering. Covers sparse representations, compressed sensing and sparse regression, so viewers understand how signals can be recovered from few measurements and how sparse models are fitted.
More resources on Signal Processing
dsprelated.com
DSPRelated.com is a comprehensive digital signal processing resource offering tutorials, articles, code samples, and a community forum on DSP theory and practical applications, covering topics like filters, transforms, audio DSP, and MATLAB/C implementations.
soundonsound.com
Sound On Sound is a long-running audio recording magazine and website. It publishes in-depth tutorials, gear reviews, and practical how-to articles on recording, mixing, mastering, microphone technique, DAWs, plugins, and studio setup.
DSP Guide
Steven W. Smith's complete DSP textbook, free in full online. Covers convolution, the Fourier transform, digital filter design, and audio and image applications, with worked examples that let a reader implement filters without heavy mathematics.
But what is the Fourier Transform? A visual introduction.
3Blue1Brown's animated introduction to the Fourier transform, building it from winding a signal around a circle to find frequency components. After watching, you can picture why the transform separates a waveform into constituent frequencies.
Digital Signal Processing 1: Basic Concepts and Algorithms
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.
Audio Signal Processing for Music Applications
Spectral analysis and synthesis of musical sound, covering the DFT, STFT, sinusoidal and harmonic models, and their use in transforming audio. Python exercises with the sms-tools package let students analyze, modify, and resynthesize real recordings.