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.
Generative adversarial networks for audio equalization: An evaluation study / Pepe, G.; Gabrielli, L.; Squartini, S.; Cattani, L.; Tripodi, C.. - ELETTRONICO. - (2020). (Intervento presentato al convegno 148th Audio Engineering Society International Convention 2020 tenutosi a aut nel 2020).
Generative adversarial networks for audio equalization: An evaluation study
Pepe G.;Gabrielli L.;Squartini S.;
2020-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
