In this paper we propose a neural network-based approach for audio equalization inside a car cabin. We consider the Generative Adversarial approach to generate FIR filters for binaural equalization at the driver listening position of the sound produced by multiple loudspeakers. The neural network is optimized to generate equalizing filters able to achieve a flat frequency response at one control position in a time-invariant scenario. Results are analyzed in the frequency domain, comparing the achieved frequency response with the desired one. Compared to previous works, the proposed approach provides better results with a very low error compared to the target response.

Authors: Pepe, Giovanni; Gabrielli, Leonardo; Squartini, Stefano; Cattani, Luca; Tripodi, Carlo
Affiliations: Università Politecnica Delle Marche, ASK Industries Spa; Università Politecnica delle Marche; Università Politecnica delle Marche; ASK Industries Spa; ASK Industries Spa(See document for exact affiliation information.)
AES Convention: 148 (May 2020) Paper Number: 10367
Publication Date: May 28, 2020
Subject: Network

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