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G. Pepe, L. Gabrielli, S. Squartini, L. Cattani, and C. Tripodi, "Generative Adversarial Networks for Audio Equalization: an evaluation study," Paper 10367, (2020 May.). doi: G. Pepe, L. Gabrielli, S. Squartini, L. Cattani, and C. Tripodi, "Generative Adversarial Networks for Audio Equalization: an evaluation study," Paper 10367, (2020 May.). doi:
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.

@article{pepe2020generative,
author={pepe, giovanni and gabrielli, leonardo and squartini, stefano and cattani, luca and tripodi, carlo},
journal={journal of the audio engineering society},
title={generative adversarial networks for audio equalization: an evaluation study},
year={2020},
volume={},
number={},
pages={},
doi={},
month={may},} @article{pepe2020generative,
author={pepe, giovanni and gabrielli, leonardo and squartini, stefano and cattani, luca and tripodi, carlo},
journal={journal of the audio engineering society},
title={generative adversarial networks for audio equalization: an evaluation study},
year={2020},
volume={},
number={},
pages={},
doi={},
month={may},
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.},}

TY - paper
TI - Generative Adversarial Networks for Audio Equalization: an evaluation study
SP - EP -
AU - Pepe, Giovanni
AU - Gabrielli, Leonardo
AU - Squartini, Stefano
AU - Cattani, Luca
AU - Tripodi, Carlo
PY - 2020
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2020 TY - paper
TI - Generative Adversarial Networks for Audio Equalization: an evaluation study
SP - EP -
AU - Pepe, Giovanni
AU - Gabrielli, Leonardo
AU - Squartini, Stefano
AU - Cattani, Luca
AU - Tripodi, Carlo
PY - 2020
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2020
AB - 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.