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Generative Adversarial Networks for Audio Equalization: an evaluation study

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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.

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AES - Audio Engineering Society