Neural third-octave graphic equalizer

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

Researchers

Research units

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. The
method is easy to apply, and may thus lead to widespread use of accurate digital graphic equalizers.

Details

Original languageEnglish
Title of host publicationProceedings of the International Conference on Digital Audio Effects
Publication statusPublished - 2 Sep 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Digital Audio Effects - Birmingham, United Kingdom
Duration: 2 Sep 20196 Sep 2019
Conference number: 22

Publication series

NameProceedings of the International Conference on Digital Audio Effects
ISSN (Print)2414-6382
ISSN (Electronic)2413-6689

Conference

ConferenceInternational Conference on Digital Audio Effects
Abbreviated titleDAFX
CountryUnited Kingdom
CityBirmingham
Period02/09/201906/09/2019

ID: 36768375