Acoustics education and the impact and concerns of AI tools

Listening is central to acoustics education for the sound source and receiver. Teachers are introducing AI in all fields, and acoustics education is no exception. Visualizations of sound require tools such as AI to represent human responses to sound, such as music, and spectral analysis is critical and practical for visualizing through AI tools. AI cannot analyze and visualize accurate acoustics data, and human listening is necessary for results that are not flawed. The psychoacoustic aspect of acoustics requires protection because listening involves democracy, agency, inclusion, expertise, and details that impact cultural heritage identity across regions and national identity. An example is pulses experienced in between the physically heard beats of rhythms—low meter frequencies that initiate meter and make us move. Listeners experience these low meter frequencies 2:1 and 3:1, with regular pulse division connected as meters, though not physically heard, but felt. Meter is the reason we move to music! Richard Cohn’s recent redefinition of meter as sound and experienced through listening reassesses how listeners visually display meter through a contemporary meter theory tool, the ski-hill graph. The session discusses ski-hill graphs, modern AI tools for acoustics education, and the impacts and concerns for acoustics education.