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Thursday, October 28 • 9:00pm - Friday, December 3 • 6:00pm
Automatic Loudspeaker Room Equalization Based On Sound Field Estimation with Artificial Intelligence Models

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In-room loudspeaker equalization requires a significant amount of microphone positions in order to characterize the sound field in the room. This can be a cumbersome task for the user. This paper proposes the use of artificial intelligence to automatically estimate and equalize, without user interaction, the in-room response. To learn the relationship between loudspeaker near-field response and total sound power, or energy average over the listening area, a neural network was trained using room measurement data. Loudspeaker near-field SPL at discrete frequencies was the input data to the neural network. The approach has been tested in a subwoofer, a full-range loudspeaker, and a TV. Results showed that the in-room sound field can be estimated within 1--2 dB average standard deviation.

Speakers
avatar for Adrian Celestinos

Adrian Celestinos

Samsung Research America
YL

Yuan Li

Samsung Research America
VM

Victor Manuel Chin Lopez

Samsung Research Tijuana


Thursday October 28, 2021 9:00pm - Friday December 3, 2021 6:00pm EST
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