One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization

Stylianos I. Mimilakis, Nicholas J. Bryan, Paris Smaragdis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Audio production is a difficult process for many people], [and properly manipulating sound to achieve a certain effect is non-trivial. In this paper], [we present a method that facilitates this process by inferring appropriate audio effect parameters in order to make an input recording sound similar to an unrelated reference recording. We frame our work as a form of parametric style transfer that], [by design], [leverages existing audio production semantics and manipulation algorithms], [avoiding several issues that have plagued audio style transfer algorithms in the past. To demonstrate our approach], [we consider the task of controlling a parametric], [four-band infinite impulse response equalizer and show that we are able to predict the parameters necessary to transform the equalization style of one recording to another. The framework we present], [however], [is applicable to a wider range of parametric audio effects.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages256-260
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Externally publishedYes
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

Keywords

  • Parametric style transfer
  • deep learning
  • one-shot learning
  • parametric equalization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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