{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/123116"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/123116","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Algorithmic design of wind instrument shape via 3D FDTD and deep learning","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_html":"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_has_math":false,"creators":["Wang, Larry(Larry Z.)"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Justin Solomon."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:41Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/123116","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Justin Solomon."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/123116"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Algorithmic design of wind instrument shape via 3D FDTD and deep learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Justin Solomon."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Wang, Larry(Larry Z.)"],"dc:date.accessioned":["2019-12-05T18:04:23Z"],"dc:date.available":["2019-12-05T18:04:23Z"],"dc:date.issued":["2019"],"dc:description":["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)."],"dc:description.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."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/123116"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Algorithmic design of wind instrument shape via 3D FDTD and deep learning"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:21:41Z"}