Neural third-octave graphic equalizer

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Neural third-octave graphic equalizer. / Rämö, Jussi; Välimäki, Vesa.

Proceedings of the International Conference on Digital Audio Effects. University of Birmingham, 2019. (Proceedings of the International Conference on Digital Audio Effects).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Rämö, J & Välimäki, V 2019, Neural third-octave graphic equalizer. in Proceedings of the International Conference on Digital Audio Effects. Proceedings of the International Conference on Digital Audio Effects, University of Birmingham, International Conference on Digital Audio Effects, Birmingham, United Kingdom, 02/09/2019.

APA

Rämö, J., & Välimäki, V. (2019). Neural third-octave graphic equalizer. In Proceedings of the International Conference on Digital Audio Effects (Proceedings of the International Conference on Digital Audio Effects). University of Birmingham.

Vancouver

Rämö J, Välimäki V. Neural third-octave graphic equalizer. In Proceedings of the International Conference on Digital Audio Effects. University of Birmingham. 2019. (Proceedings of the International Conference on Digital Audio Effects).

Author

Rämö, Jussi ; Välimäki, Vesa. / Neural third-octave graphic equalizer. Proceedings of the International Conference on Digital Audio Effects. University of Birmingham, 2019. (Proceedings of the International Conference on Digital Audio Effects).

Bibtex - Download

@inproceedings{76a9aac585f3418c8c72ff1b07730793,
title = "Neural third-octave graphic equalizer",
abstract = "This paper proposes to speed up the design of a third-order graphic equalizer by training a neural network to imitate its gain optimization. Instead of using the neural network to learn to design the graphic equalizer by optimizing its magnitude response, we present the network only with example command gains and the corresponding optimized gains, which are obtained with a previously proposed least-squares-based method. We presented this idea recently for the octave graphic equalizer with 10 band filters and extend it here to the third-octave case. Instead of a network with a single hidden layer, which we previously used, this task appears to require two hidden layers. This paper shows that good results can be reached with a neural network having 62 and 31 units in the first and the second hidden layer, respectively. After the training, the resulting network can quickly and accurately design a third-order graphic equalizer with a maximum error of 1.2 dB. The computing of the filter gains is over 350 times faster with the neural network than with the original optimization method. Themethod is easy to apply, and may thus lead to widespread use of accurate digital graphic equalizers.",
author = "Jussi R{\"a}m{\"o} and Vesa V{\"a}lim{\"a}ki",
year = "2019",
month = "9",
day = "2",
language = "English",
series = "Proceedings of the International Conference on Digital Audio Effects",
publisher = "University of Birmingham",
booktitle = "Proceedings of the International Conference on Digital Audio Effects",

}

RIS - Download

TY - GEN

T1 - Neural third-octave graphic equalizer

AU - Rämö, Jussi

AU - Välimäki, Vesa

PY - 2019/9/2

Y1 - 2019/9/2

N2 - This paper proposes to speed up the design of a third-order graphic equalizer by training a neural network to imitate its gain optimization. Instead of using the neural network to learn to design the graphic equalizer by optimizing its magnitude response, we present the network only with example command gains and the corresponding optimized gains, which are obtained with a previously proposed least-squares-based method. We presented this idea recently for the octave graphic equalizer with 10 band filters and extend it here to the third-octave case. Instead of a network with a single hidden layer, which we previously used, this task appears to require two hidden layers. This paper shows that good results can be reached with a neural network having 62 and 31 units in the first and the second hidden layer, respectively. After the training, the resulting network can quickly and accurately design a third-order graphic equalizer with a maximum error of 1.2 dB. The computing of the filter gains is over 350 times faster with the neural network than with the original optimization method. Themethod is easy to apply, and may thus lead to widespread use of accurate digital graphic equalizers.

AB - This paper proposes to speed up the design of a third-order graphic equalizer by training a neural network to imitate its gain optimization. Instead of using the neural network to learn to design the graphic equalizer by optimizing its magnitude response, we present the network only with example command gains and the corresponding optimized gains, which are obtained with a previously proposed least-squares-based method. We presented this idea recently for the octave graphic equalizer with 10 band filters and extend it here to the third-octave case. Instead of a network with a single hidden layer, which we previously used, this task appears to require two hidden layers. This paper shows that good results can be reached with a neural network having 62 and 31 units in the first and the second hidden layer, respectively. After the training, the resulting network can quickly and accurately design a third-order graphic equalizer with a maximum error of 1.2 dB. The computing of the filter gains is over 350 times faster with the neural network than with the original optimization method. Themethod is easy to apply, and may thus lead to widespread use of accurate digital graphic equalizers.

M3 - Conference contribution

T3 - Proceedings of the International Conference on Digital Audio Effects

BT - Proceedings of the International Conference on Digital Audio Effects

PB - University of Birmingham

ER -

ID: 36768375