2026-09-19T16:02:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1231162026-06-06T00:54:58Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Justin Solomon.
Wang, Larry(Larry Z.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-12-05T18:04:23Z
2019-12-05T18:04:23Z
2019
2019
https://hdl.handle.net/1721.1/123116
1128185621
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-60).
Traditional design of wind instruments centers around simple shapes such as tubes and cones, whose acoustic properties are well understood and are easily fabricated with traditional manufacturing methods. The advent of additive manufacturing enables the realization of highly complex geometries and new wind instruments with unique sound qualities. While simulation software exists to predict the sound of wind instruments given their shape, the inverse problem of generating a shape that creates a desired sound is challenging given the computational cost of 3D acoustic simulations. In this work we create a fast 3D acoustic wind instrument simulator using GPU acceleration. In addition, we use deep learning to solve the inverse problem of generating a 3D shape that roughly approximates a desired sound when played as a single-reed instrument. Finally we develop an automatic method for determining pitch hole locations for a given shape to generate playable instruments.
by Larry Wang.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
60 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Algorithmic design of wind instrument shape via 3D FDTD and deep learning
Algorithmic design of wind instrument shape via three-dimensional Finite Difference Time Domain and deep learning
Thesis
application/pdf
2019-12-05T18:04:22Z
Publication
Master
EECS
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="860525b5-9e68-4170-a79a-381b40421710">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Algorithmic design of wind instrument shape via 3D FDTD and deep learning</Title>
<Subtitle>Algorithmic design of wind instrument shape via three-dimensional Finite Difference Time Domain and deep learning</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2019</PublicationDate>
<Authors>
<Author>
<DisplayName>Wang, Larry(Larry Z.)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>Traditional design of wind instruments centers around simple shapes such as tubes and cones, whose acoustic properties are well understood and are easily fabricated with traditional manufacturing methods. The advent of additive manufacturing enables the realization of highly complex geometries and new wind instruments with unique sound qualities. While simulation software exists to predict the sound of wind instruments given their shape, the inverse problem of generating a shape that creates a desired sound is challenging given the computational cost of 3D acoustic simulations. In this work we create a fast 3D acoustic wind instrument simulator using GPU acceleration. In addition, we use deep learning to solve the inverse problem of generating a 3D shape that roughly approximates a desired sound when played as a single-reed instrument. Finally we develop an automatic method for determining pitch hole locations for a given shape to generate playable instruments.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>