Back to results

Massachusetts Institute of Technology

Machine Audition Curriculum and Real-Time Music Accompaniment

Abstract

dc:description.abstract

A machine audition curriculum was created as part of the MIT Media Lab’s Artificial Intelligence Education initiative. This curriculum was geared towards middle school students to help them understand how humans and machines perceive sound, and allow them to apply this knowledge to create and analyze their own music. This thesis presents the tools created to aid in the teaching of this curriculum: a new music audition Scratch extension. This extension introduces the ability to create and analyze music, as well as the integration of Google Magenta, a machine learning library that allows students to generate new music or accompany music that they have created. Through the use of this Scratch extension, it was possible to pilot the machine audition curriculum with middle school students and show that they were able to better understand signal properties, create and analyze their own music, and understand the similarities and differences between human and machine audition.

Degree

thesis:*
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
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Hussein, Nada
Advisor dc:contributor.advisor
  • Breazeal, Cynthia

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright MIT

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/139888
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/139888

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Hussein, Nada. Machine Audition Curriculum and Real-Time Music Accompaniment. Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/139888