Multidimensional signal processing
Perceptual-based 3D sound localization is the application of knowledge of the human auditory system to develop 3D sound localization technology. (Wikipedia).
PSY108 - Pre-Lexical Processing I: Speech Perception
This E-Lecture discusses the initial stages of speech perception, in articular the invariable properties of the spoken and written signal, so called perceptual cues. A second aspect concerns the modeling of speech perception, i.e. the discussion of active and passive perception models.
From playlist VLC301 - Psycholinguistics
Surround Sound's Point of Origin
From the October 24th mediaX Sensing and Tracking for 3D Narratives Conference, Chris Chafe, Director of the Center for Computer Research in Music and Acoustics at Stanford University looks to the next couple of years and the potential for the growth and deployment of surround sound in med
From playlist Sensing and Tracking for 3D Narratives
My 3D printed DIY speaker design. Tutorial: https://youtu.be/bmT21EHKQuM
From playlist 3D Printing
This computer science lesson describes how sound is digitally encoded and stored by a computer. It begins with a discussion of the nature of sound in air, namely, a longitudinal wave of compressions and rarefactions. It then explains how sound can be captured by a dynamic microphone, whi
From playlist GCSE Computer Science
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist 3D Printing
Building 3D Printer DIY sound Proof Booth
I built a sound proof booth for my Cube 2nd generation 3D printer to help lower the noise. I can really tell a difference. music by: 216V0 https://www.youtube.com/watch?v=wNQJyHAdS48
From playlist 3D Printing
NuFormer 3D Video Mapping Projection on Buildings
NuFormer 3D Video Mapping Projection on Buildings Impressive and stylish projections on buildings, a renewing way of communicating. For those who want to carry out a message in a striking and visually attractive way with guaranteed exposure: 3D Projection on buildings is the communication
From playlist Projection Mapping inspirations
Stanford Seminar - Driving Exploratory Visualization through Perception & Cognition
Danielle Szafir University of North Carolina - Chapel Hill November 5, 2021 Visualizations allow analysts to rapidly explore and make sense of their data. The ways we visualize data directly influence the conclusions we draw and decisions we make; however, our knowledge of how visualizati
From playlist Stanford Seminars
Recorded: Spring 2014 Lecturer: Dr. Erin M. Buchanan Materials: created for Memory and Cognition (PSY 422) using Smith and Kosslyn (2006) Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdoom.com/page/other-courses/
From playlist PSY 422 Memory and Cognition with Dr. B
Lecture 20: Introduction to Animation (CMU 15-462/662)
Full playlist: https://www.youtube.com/playlist?list=PL9_jI1bdZmz2emSh0UQ5iOdT2xRHFHL7E Course information: http://15462.courses.cs.cmu.edu/
From playlist Computer Graphics (CMU 15-462/662)
Lecture 11/16 : Hopfield nets and Boltzmann machines
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 11A Hopfield Nets 11B Dealing with spurious minima in Hopfield Nets 11C Hopfield Nets with hidden units 11D Using stochastic units to improve search 11E How a Boltzmann Machine models data
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 11C : Hopfield Nets with hidden units
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 11C : Hopfield Nets with hidden units
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 11.3 — Hopfield nets with hidden units [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
CS231n Lecture 1 - Introduction and Historical Context
Lecture 1 - Intro to Computer Vision, historical context. Please see the following link for detailed course material http://cs231n.stanford.edu/ Join discussions on Facebook: https://www.facebook.com/groups/DataScienceGroup/
From playlist CS231N - Convolutional Neural Networks
Panel: Frontiers in Technologies
From the October 24th mediaX Sensing and Tracking for 3D Narratives Conference, this panel consisting of Jay Borenstein a Lecturer in Computer Science at Stanford University, Vincent Chang a SR. Engineer with ITRI, Nick Haber a Post Doc Scholar with the Wall Lab at Stanford University and
From playlist Sensing and Tracking for 3D Narratives
This E-Lecture discusses the central questions of speech perception: How do we cope with the complexity of the sound signal and how can we model the perceptual process?
From playlist VLC102 - Speech Science
Texture Mapping & Polygon Rasterizing Tutorial (2/2) [C++20]
Textured polygons are the foundation of nearly all 3D games in existence. Used before even 3D-capable GPUs were a thing, they were rendered using nothing but software. How was that achieved? Let’s explore an easy and intuitive method. We create an extensible 3D polygon rasterizer using not
From playlist 3D Rendering Tutorial
How to derive the doppler effect formula for sound
The Doppler shift formula allows you to determine the perceived frequency of a sound source, but how is it determined and derived. This video goes through the derivation on how it is worked out. Other videos.... a doppler demo - https://youtu.be/vio18enhGwo a doppler animation - https://y
From playlist Waves and Thermodynamics
Resolution-robust Large Mask Inpainting with Fourier Convolutions (w/ Author Interview)
#lama #inpainting #deeplearning At the end of the video is an interview with the paper authors! LaMa is a system that is amazing at removing foreground objects from images, especially when those objects cover a large part of the image itself. LaMa is specifically trained to reconstruct la
From playlist Papers Explained