Physically Derived Synthesis Model of an Edge Tone
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R. Selfridge, JO. D.. Reiss, and EL. J.. Avital, "Physically Derived Synthesis Model of an Edge Tone," Paper 9956, (2018 May.). doi:
R. Selfridge, JO. D.. Reiss, and EL. J.. Avital, "Physically Derived Synthesis Model of an Edge Tone," Paper 9956, (2018 May.). doi:
Abstract: The edge tone is the sound generated when a planar jet of air from a nozzle comes into contact with a wedge and a number of physical conditions are met. Fluid dynamics equations were used to synthesize authentic edge tones without the need for complex computation. A real-time physically derived synthesis model was designed using the jet airspeed and nozzle exit-to-wedge geometry. We compare different theoretical equations used to predict the tone frequency. A decision tree derived from machine learning based on previously published experimental results was used to predict the correct mode of operation. Results showed an accurate implementation for mode selection and highlighted areas where operation follows or deviates from previously published data.
@article{selfridge2018physically,
author={selfridge, rod and reiss, joshua d. and avital, eldad j.},
journal={journal of the audio engineering society},
title={physically derived synthesis model of an edge tone},
year={2018},
volume={},
number={},
pages={},
doi={},
month={may},}
@article{selfridge2018physically,
author={selfridge, rod and reiss, joshua d. and avital, eldad j.},
journal={journal of the audio engineering society},
title={physically derived synthesis model of an edge tone},
year={2018},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={the edge tone is the sound generated when a planar jet of air from a nozzle comes into contact with a wedge and a number of physical conditions are met. fluid dynamics equations were used to synthesize authentic edge tones without the need for complex computation. a real-time physically derived synthesis model was designed using the jet airspeed and nozzle exit-to-wedge geometry. we compare different theoretical equations used to predict the tone frequency. a decision tree derived from machine learning based on previously published experimental results was used to predict the correct mode of operation. results showed an accurate implementation for mode selection and highlighted areas where operation follows or deviates from previously published data.},}
TY - paper
TI - Physically Derived Synthesis Model of an Edge Tone
SP -
EP -
AU - Selfridge, Rod
AU - Reiss, Joshua D.
AU - Avital, Eldad J.
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2018
TY - paper
TI - Physically Derived Synthesis Model of an Edge Tone
SP -
EP -
AU - Selfridge, Rod
AU - Reiss, Joshua D.
AU - Avital, Eldad J.
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2018
AB - The edge tone is the sound generated when a planar jet of air from a nozzle comes into contact with a wedge and a number of physical conditions are met. Fluid dynamics equations were used to synthesize authentic edge tones without the need for complex computation. A real-time physically derived synthesis model was designed using the jet airspeed and nozzle exit-to-wedge geometry. We compare different theoretical equations used to predict the tone frequency. A decision tree derived from machine learning based on previously published experimental results was used to predict the correct mode of operation. Results showed an accurate implementation for mode selection and highlighted areas where operation follows or deviates from previously published data.
The edge tone is the sound generated when a planar jet of air from a nozzle comes into contact with a wedge and a number of physical conditions are met. Fluid dynamics equations were used to synthesize authentic edge tones without the need for complex computation. A real-time physically derived synthesis model was designed using the jet airspeed and nozzle exit-to-wedge geometry. We compare different theoretical equations used to predict the tone frequency. A decision tree derived from machine learning based on previously published experimental results was used to predict the correct mode of operation. Results showed an accurate implementation for mode selection and highlighted areas where operation follows or deviates from previously published data.
Authors:
Selfridge, Rod; Reiss, Joshua D.; Avital, Eldad J.
Affiliations:
Queen Mary University of London, London, UK; University of Edinburgh, Edinburgh, UK(See document for exact affiliation information.)
AES Convention:
144 (May 2018)
Paper Number:
9956
Publication Date:
May 14, 2018Import into BibTeX
Subject:
Audio Coding, Analysis, and Synthesis
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=19473