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Physically Based Sound UNC-CH 259 Physically Based Class lecture, April 1st 2002 Vincent Scheib Overview Summary & Introduction to Audio Case study of 3 recent papers Audio Synthesis Overview: Adapted from the Siggraph 2000 Course Notes by Perry R. Cook found here: http://www.cs.princeton.edu/~prc/CookSig00.pdf Audio Synthesis Overview: Views of Sound Time Domain x(t) From physics Related to production Frequency Domain From math Related to perception x(f) Audio Synthesis Overview: Time Domain Forces cause acceleration > velocity > > position changes over time Position changes cause sound waves over time Audio Synthesis Overview: Frequency Domain Physical systems have vibration modes (damped oscillations) Think of the modes on a string: The frequency determines the mode Specific Frequencies appear over time Solutions are sums of damped sines Audio Synthesis Overview: Spectra Plots over frequency and time of Magnitude Phase Overview of not-so-physicallybased work Magnitude – Generate varying magnitudes over time Stochastic – Random methods, using statistics Residual – The difference between simulated and real instrument Transients – Swells in sound across all frequencies Subtractive Synthesis (formants) – Shape the global frequency envelope Physically Based Methods Modal Synthesis Specific Models String, Tube, Bar, Plate Human head Whistle, Maraca What I’m not talking about Enviromental analysis/simulation How does sound propagate from a source to your perception: Direct transmission through media (air) Reflected transmission (similar to global illumination) Affected by shape of ears and your thick head You have 2 ears, two sources Recent Papers Synthesizing Sounds from Physically Based Motion James O’Brien, Cook, Essl – siggraph 2001 FoleyAutomatic: Physically-based Sound Effects for Interactive Simulation and Animation Kees van den Doel, Kry, Pai – siggraph 2001 Real-Time Modeling of Sound and Deformation James O’Brien – GDC 2002 Synthesizing Sounds from P. B. M. Basic Idea Deformable simulation with really tiny time steps Compute sound from change in pressure of air along object’s surface Synthesizing Sounds from P. B. M. Requirements of System Temporal Resolution must capture 20,000Hz Deformation Modeling Rigid body, and intertia-less, solutions not sufficient Surface Representation Requires explicit surface, to solve for air vibration Physical Accuracy Audio more sensitive than just animation Synthesizing Sounds from P. B. M. Getting Pressure For each triangle, get a normal and velocity Pressure from velocity dot normal pressure = velocity . Normal Also compute area of that triangle Filter out in-audible frequencies They cause unwanted ailiasing and DC components. Synthesizing Sounds from P. B. M. Hearing the Sound Simple version in this paper: Record only direct “line of sight” sound Diminish by “visible” area Divide by distance from viewer – Should be distance squared, but microphones and ears respond to √(sound wave energy) Account for delay by computing distance to camera and using speed of sound. Synthesizing Sounds from P. B. M. Results Movie http://www.cs.berkeley.edu/~job/Projects/SoundGen/video.html Several objects being bonked to make noise. Takes a LONG TIME to compute. – Hours! – Days! FoleyAutomatic Basic Idea Real-time Uses modal models Special cases for: Impact Rolling Sliding FoleyAutomatic Modal Resonance Models Modal model consists of three things F = N Modal frequencies D = N Decay rates A = N by K gains, – N = number of modal frequencies modeled Decay – K = number of discrete locations on an object Frequency Gain Outputk(T) = n=1..N( Ank e-Dn*Tsin(2pi FnT) ) FoleyAutomatic Requirements of System: Multi-body Dynamics Rolling, sliding contacts Smooth surfaces Smooth continuous contact This paper used Loop subdivision surfaces FoleyAutomatic Impact! Two most distinguishing characteristics: Energy transfer – Force magnitude Hardness – Force duration (shorter == harder) FoleyAutomatic Scraping & Sliding Play back a recording of scraping noise at variable rate based on velocity recording from pre-simulation or acquired data Audio volume = (velocity * normal_force) FoleyAutomatic Sound Profile Fractal noise amplitude of harmonics fit to real world data. FoleyAutomatic Rolling Very uncertain area Rolling has “softer contacts”, thus only use the low frequecies of a sound profile? Works okay, not great Rolling contact forces seem to be tied to the modes of the objects – audio feedback into forces – no longer linear – AAHHH!! FoleyAutomatic Results Movie http://www.cs.ubc.ca/~pai/movies/foleyautomatic.mpg Real-Time Modeling of Sound and Deformation Slides & movies available at http://www.cs.berkeley.edu/~job/Talks/