Algorithmic design of wind instrument shape via 3D FDTD and deep learning

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Wang, Larry(Larry Z.)

Advisor dc:contributor.advisor

• Justin Solomon.

• Electrical Engineering and Computer Science.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123116

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123116