Duffing s equation as an excitation mechanism for plucked string instrument models
Download
1 / 54

Duffing - PowerPoint PPT Presentation


  • 324 Views
  • Updated On :

Duffing’s Equation as an Excitation Mechanism for Plucked String Instrument Models. by Justo A. Gutierrez Master’s Research Project Music Engineering Technology University of Miami School of Music December 1, 1999. Purpose.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Duffing' - medwin


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Duffing s equation as an excitation mechanism for plucked string instrument models l.jpg

Duffing’s Equation as an Excitation Mechanism for Plucked String Instrument Models

by

Justo A. Gutierrez

Master’s Research Project

Music Engineering Technology

University of Miami School of Music

December 1, 1999


Purpose l.jpg
Purpose String Instrument Models

  • The objective of this study is to provide the basis for a new excitation mechanism for plucked string instrument models which utilizes the classical nonlinear system described in Duffing’s Equation.


Advantages l.jpg
Advantages String Instrument Models

  • Using Duffing’s Equation provides a means to use a nonlinear oscillator as an excitation

  • A mathematical model lends itself to user control

  • Removes the need for saving samples in a wavetable


Overview l.jpg
Overview String Instrument Models

  • Plucked String Instrument Modeling

  • Excitation Modeling with Duffing’s Equation

  • Model Performance and Analysis


Wavetable synthesis l.jpg
Wavetable Synthesis String Instrument Models

  • Method of synthesis that uses tables of waveforms that are finely sampled

  • Desired waveform is chosen and repeated over and over producing a purely periodic signal

  • Algorithm written as: Yt = Yt-p

  • p is periodicity parameter

  • frequency of the tone is fs/p


The string model l.jpg
The String Model String Instrument Models

  • z-L is delay line of length L

  • H(z) is the loop filter

  • F(z) is the allpass filter

  • x(n) and y(n) are the excitation and output signals respectively


Length of string l.jpg
Length of String String Instrument Models

  • Effective delay length determines fundamental frequency of output signal

  • Delay line length (in samples) is L = fs/f0


The comb filter l.jpg
The Comb Filter String Instrument Models

  • Works by adding, at each sample time, a delayed and attenuated version of the past output


Standing wave analogy l.jpg
Standing Wave Analogy String Instrument Models

  • Poles of the comb filter occur in the z-plane at 2np/L

  • This is the same as the natural resonant frequencies for a string tied at both ends

  • Does not sound like a vibrating string because it is a perfectly periodic waveform

  • Does not take into account that high frequencies decay much faster than slow ones for vibrating strings


The loop filter l.jpg
The Loop Filter String Instrument Models

  • Idea is to insert a lowpass filter into the feedback loop of the comb filter so that high-frequency components are diminished relative to low-frequency components every time the past output signal returns

  • Original Karplus-Strong algorithm used a two-tap averager that was simple and effective


Loop filter continued l.jpg
Loop Filter (continued) String Instrument Models

  • Valimaki et al proposed using an IIR lowpass filter to simulate the damping characteristics of a physical string

  • Loop filter coefficients can be changed as a function of string length and other parameters

  • H1(z) = g(1+a1)/(1+a1z-1)


Loop filter signal flowchart l.jpg
Loop Filter Signal Flowchart String Instrument Models



Loop filter impulse responses l.jpg
Loop Filter Impulse Responses String Instrument Models


The allpass filter l.jpg
The Allpass Filter String Instrument Models

  • Used to fine-tune the pitch of the string model

  • If feedback loop were only to contain a delay line and lowpass filter, total delay would be the sum of integer delay line plus the delay of the lowpass filter

  • Fundamental frequency of fs/D is usually not an integer number of samples


Allpass filter continued l.jpg
Allpass Filter (continued) String Instrument Models

  • Fundamental frequency is then given by f1 = fs/(D+d) where d is fractional delay

  • Allpass filters introduce delay but pass frequencies with equal weight

  • Transfer function is H(z) = (z-1+a)/(1+az-1)

  • a = (1-d)/(1+d)


Allpass phase response l.jpg
Allpass Phase Response String Instrument Models


Allpass delay response l.jpg
Allpass Delay Response String Instrument Models


Inverse filtering l.jpg
Inverse Filtering String Instrument Models

  • KS algorithm used a white noise burst as excitation for plucked string because it provided high-frequency content as a real pluck would provide

  • Valimaki et al found a pluck signal by filtering the output through the inverted transfer function of the string system


Inverse filtering continued l.jpg
Inverse Filtering (continued) String Instrument Models

  • The transfer function for the general string model can be given as S(z) = 1/[1-z-LF(z)H(z)]

  • The inverse filter is simply S-1(z) = 1/S(z)


Inverse filtering procedure l.jpg
Inverse Filtering Procedure String Instrument Models

  • Obtain residual by inverse filtering

  • Truncate the first 50-100 ms of the residual

  • Use the truncated signal as the excitation to the string model

  • Run the string model


Steel string guitar sample l.jpg
Steel-string Guitar Sample String Instrument Models


Residual after inverse filtering l.jpg
Residual After Inverse Filtering String Instrument Models


Truncated residual signal l.jpg
Truncated Residual Signal String Instrument Models


Resynthesized guitar l.jpg
Resynthesized Guitar String Instrument Models


Duffing s equation l.jpg
Duffing’s Equation String Instrument Models

  • In 1918, Duffing introduced a nonlinear oscillator with a cubic stiffness term to describe the hardening spring effect in many mechanical problems

  • It is one of the most common examples in the study of nonlinear oscillations


Duffing s equation continued l.jpg
Duffing’s Equation (continued) String Instrument Models

  • The form used for this study is from Moon and Holmes, which is one in which the linear stiffness term is negative so that x” + dx - x + x3 = g cos wt.

  • This model was used to describe the forced oscillations of a ferromagnetic beam buckled between the nonuniform field of two permanent magnets



Modeling the excitation l.jpg
Modeling the Excitation String Instrument Models

  • For this experiment, the coefficients in Moon and Holmes’ modification of Duffing’s Equation were adjusted to produce the desired residuals

  • The Runge-Kutta method was the numerical method used to calculate Duffing’s Equation


Procedure for manipulating duffing s equation l.jpg
Procedure for manipulating Duffing’s Equation String Instrument Models

  • Generate a waveform of desired frequency with (x, y). f10y is a good rule of thumb for starters.

  • Adjust the damping coefficient so that its envelope resembles the desired waveform’s

  • Adjust b, g, and w to shape the waveform, holding one constant to change the other

  • Normalize the waveform to digital maximum



Synthesizing the plucked string l.jpg
Synthesizing the Plucked String String Instrument Models



Timbral characteristics l.jpg
Timbral Characteristics Excitation

  • Synthesized guitar from Duffing’s Equation very similar to that from inverse filtering

  • Frequency of both residuals different from pitch of synthesized stringsinharmonicity

  • Sonograms of both residuals also very similar


Sonogram for guitar inverse filtering l.jpg
Sonogram for Guitar Excitation(Inverse Filtering)


Sonogram for guitar duffing s equation l.jpg
Sonogram for Guitar Excitation(Duffing’s Equation)


Tuning performance harmony l.jpg
Tuning Performance (Harmony) Excitation

  • For individual pitches, the algorithm played fairly close to being in tune (perhaps slightly sharp). The allpass filter parameters can be adjusted to remedy this.

  • The C major chord played very well in tune, sounding very consonant with no apparent beats.


Tuning performance range l.jpg
Tuning Performance (Range) Excitation

  • To test effective range of the algorithm, the lowest and highest pitches in a guitar’s range were synthesized.

  • Low E played in tune by itself. High E was flat.

  • This was more readily apparent when sounded together.


Summary of tuning performance l.jpg
Summary of Tuning Performance Excitation

  • Algorithm performed as expected; it performed like Karplus-Strong; high frequencies tend to go flat, and this would have to be accounted for in the overall system.


Changing damping coefficient l.jpg
Changing Damping Coefficient Excitation

  • Changing the damping coefficient can have pronounced effect on timbre of sound, specifically difference between type of pick used and type of string

  • The damping coefficient was adjusted to attempt to produce different sounds


Synthesized residual d 0 2 l.jpg
Synthesized Residual ( Excitationd = 0.2)


Synthesized guitar d 0 2 l.jpg
Synthesized Guitar ( Excitationd = 0.2)


Synthesized residual d 0 5 l.jpg
Synthesized Residual ( Excitationd = 0.5)


Synthesized residual d 0 544 l.jpg
Synthesized Residual ( Excitationd = 0.5)


Summary of damping coefficient adjustments l.jpg
Summary of Damping Coefficient Adjustments Excitation

  • For d = 0.2, contribution of residual made for a very hard attack, as if picked

  • For d = 0.5, guitar tone had much softer attack, as if finger-picked

  • Sonograms confirm that the latter had more high-frequency content


Sonogram for d 0 2 l.jpg
Sonogram for Excitationd = 0.2


Sonogram for d 0 5 l.jpg
Sonogram for Excitationd = 0.5


Production of other waveforms l.jpg
Production of Other Waveforms Excitation

  • Duffing’s Equation can be used to form a variety of waveforms

  • User has some control over its behavior if properties of the oscillator can be controlled to obtain the desired waveform





Residual with strong high frequency forcing function l.jpg
Residual with Strong High-Frequency ExcitationForcing Function


Algorithm speed l.jpg
Algorithm Speed Excitation

  • For 200 MHz Pentium Pro, Karplus-Strong with an inverse filtered residual took 57.46 s. with approximately 2500 samples saved on a wavetable

  • With synthesized residual, Duffing’s Equation added only 4.057 s; total computation time increased by only about 5% with no saved samples


Conclusion l.jpg
Conclusion Excitation

  • Plucked string sounds were successfully produced

  • Model plays in tune

  • Different plucked string sounds can be produced by changing the damping coefficient

  • Algorithm is fast