Noise (electronics) | Signal processing
Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization also forms the core of essentially all lossy compression algorithms. The difference between an input value and its quantized value (such as round-off error) is referred to as quantization error. A device or algorithmic function that performs quantization is called a quantizer. An analog-to-digital converter is an example of a quantizer. (Wikipedia).
Quantization and Coding in A/D Conversion
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Real sampling systems use a limited number of bits to represent the samples of the signal, resulting in quantization of the signal amplitude t
From playlist Sampling and Reconstruction of Signals
Analysis of Quantization Error
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Modeling quantization error as uncorrelated noise. Signal to quantization noise ratio as a function of the number of bits used to represent the sign
From playlist Sampling and Reconstruction of Signals
Introduction to Signal Processing
http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Introductory overview of the field of signal processing: signals, signal processing and applications, phi
From playlist Introduction and Background
Gilles Pagès: Optimal vector Quantization: from signal processing to clustering and ...
Abstract: Optimal vector quantization has been originally introduced in Signal processing as a discretization method of random signals, leading to an optimal trade-off between the speed of transmission and the quality of the transmitted signal. In machine learning, similar methods applied
From playlist Probability and Statistics
Notation and Basic Signal Properties
http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Signals as functions, discrete- and continuous-time signals, sampling, images, periodic signals, displayi
From playlist Introduction and Background
Signal correlation functions for parameter estimation - A. Tilloy - Workshop 1 - CEB T2 2018
Antoine Tilloy (Max Plank Institut für Quantenoptik, Garching) / 17.05.2018 Signal correlation functions for parameter estimation When continuously measuring a quantum system, one is typically interested in reconstructing the quantum state in real time as a function of the measured signa
From playlist 2018 - T2 - Measurement and Control of Quantum Systems: Theory and Experiments
Determining Signal Similarities
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Find a signal of interest within another signal, and align signals by determining the delay between them using Signal Processing Toolbox™. For more on Signal Processing To
From playlist Signal Processing and Communications
Convolution and Unit Impulse Response
The Dirac delta function, the Unit Impulse Response, and Convolution explained intuitively. Also discusses the relationship to the transfer function and the Laplace Transform. Signal Analysis for Linear Systems. My Patreon page is at https://www.patreon.com/EugeneK
From playlist Physics
Convolution in the time domain
Now that you understand the Fourier transform, it's time to start learning about time-frequency analyses. Convolution is one of the best ways to extract time-frequency dynamics from a time series. Convolution can be conceptualized and implemented in the time domain or in the frequency doma
From playlist OLD ANTS #3) Time-frequency analysis via Morlet wavelet convolution
MIT MIT 6.003 Signals and Systems, Fall 2011 View the complete course: http://ocw.mit.edu/6-003F11 Instructor: Dennis Freeman License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.003 Signals and Systems, Fall 2011
Laurent Jacques/Valerio Cambareri: Small width, low distortions: quantized random projections of...
Laurent Jacques / Valerio Cambareri: Small width, low distortions: quantized random projections of low-complexity signal sets Abstract: Compressed sensing theory (CS) shows that a "signal" can be reconstructed from a few linear, and most often random, observations. Interestingly, this rec
From playlist HIM Lectures: Trimester Program "Mathematics of Signal Processing"
High Fidelity Neural Audio Compression | Paper & Code Explained
❤️ Become The AI Epiphany Patreon ❤️ https://www.patreon.com/theaiepiphany 👨👩👧👦 Join our Discord community 👨👩👧👦 https://discord.gg/peBrCpheKE In this video I cover the "High Fidelity Neural Audio Compression" paper and code. With 6 kbps they already get the same audio quality (as
From playlist Coding Videos
The Unreasonable Effectiveness of JPEG: A Signal Processing Approach
Visit https://brilliant.org/Reducible/ to get started learning STEM for free, and the first 200 people will get 20% off their annual premium subscription. Chapters: 00:00 Introducing JPEG and RGB Representation 2:15 Lossy Compression 3:41 What information can we get rid of? 4:36 Introduc
From playlist Fourier
Emmanuel Candès: Wavelets, sparsity and its consequences
Abstract: Soon after they were introduced, it was realized that wavelets offered representations of signals and images of interest that are far more sparse than those offered by more classical representations; for instance, Fourier series. Owing to their increased spatial localization at f
From playlist Abel Lectures
Artificial Intelligence per Kilowatt-hour: Max Welling, University of Amsterdam
Professor Welling is a research chair in Machine Learning at the University of Amsterdam and a Vice President Technologies at Qualcomm. He has a secondary appointment at the Canadian Institute for Advanced Research (CIFAR). He is co-founder of “Scyfer BV” a university spin-off in deep lear
From playlist AI for Social Good
Speech and Audio Processing 4: Speech Coding I - Professor E. Ambikairajah
Speech and Audio Processing Speech Coding - Lecture notes available from: http://eemedia.ee.unsw.edu.au/contents/elec9344/LectureNotes/
From playlist ELEC9344 Speech and Audio Processing by Prof. Ambikairajah
JPEG DCT, Discrete Cosine Transform (JPEG Pt2)- Computerphile
DCT is the secret to JPEG's compression. Image Analyst Mike Pound explains how the compression works. Colourspaces: https://youtu.be/LFXN9PiOGtY JPEG 'files' & Colour: https://youtu.be/n_uNPbdenRs Computer That Changed Everything (Altair 8800): https://youtu.be/6LYRgrqJgDc Problems wit
From playlist Fourier
Where have I heard that song? For us humans, it is pretty easy to recognize a recording. However, to a machine, two signals that sound the same could look totally different! In this talk, Carlo Giacometti uses the Wolfram Language to understand and explore different techniques to identify
From playlist Wolfram Technology Conference 2020
Frequency Domain Interpretation of Sampling
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Analysis of the effect of sampling a continuous-time signal in the frequency domain through use of the Fourier transform.
From playlist Sampling and Reconstruction of Signals