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.

Authors: Celestinos, Adrian; Li, Yuan; Chin Lopez, Victor Manuel
Affiliations: Samsung Research America, DMS Audio, Valencia CA, USA; Samsung Research Tijuana, Tijuana BC, Mexico(See document for exact affiliation information.)
AES Convention: 151 (October 2021) Paper Number: 10520
Publication Date: October 13, 2021 Import into BibTeX
Subject: Architectural Acoustics
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=21484

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