A regular user of a multichannel loudspeaker system in typical living rooms sets the loudspeakers in a non-uniform manner, with angles and distances that don’t necessarily follow the recommended ITU-R BS.2159-4 standard. Assuming a multichannel audio system equipped with N number of loudspeakers and M very near-field (NF) microphones attached to each loudspeaker, the user location with respect to the loudspeakers can be estimated by utilizing a supervised machine learning (ML) model. Two neural networks (NN) were trained with the human speech directivity index (DI) computed by room simulations, where the sound source was the typical directivity radiation pattern of human speech, and the receivers were the NF microphones attached to the loudspeakers. The distances between loudspeakers and the DI data was combined as input for the two NN models. One network was dedicated to estimate distances from loudspeaker to user, and the other network was dedicated to the angle estimation. The results shown a 95% confidence interval (CI) of ±1.7 cm and a CI of ±7 degrees for the incidence angle.

Authors: Celestinos, Adrian; Wang, Carren Zhongran; Chin Lopez, Victor Manuel
Affiliations: Samsung Research America; Samsung Research America; Samsung Research Tijuana(See document for exact affiliation information.)
Express Paper 138; AES Convention 155; October 2023
Publication Date: October 25, 2023 Import into BibTeX
Subject: Immersive & Spatial Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=22292

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