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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.

avatar for Adrian Celestinos

Adrian Celestinos

Samsung Research America

Yuan Li

Samsung Research America

Victor Manuel Chin Lopez

Samsung Research Tijuana

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