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

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

doi:10.1109/ICASSP40776.2020.9054108

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 proceeding › Conference 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 language	English (US)
Title of host publication	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	256-260
Number of pages	5
ISBN (Electronic)	9781509066315
DOIs	https://doi.org/10.1109/ICASSP40776.2020.9054108
State	Published - May 2020
Externally published	Yes
Event	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain Duration: May 4 2020 → May 8 2020

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2020-May
ISSN (Print)	1520-6149

Conference

Conference	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country	Spain
City	Barcelona
Period	5/4/20 → 5/8/20

Keywords

Parametric style transfer
deep learning
one-shot learning
parametric equalization

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP40776.2020.9054108

Fingerprint Dive into the research topics of 'One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization'. Together they form a unique fingerprint.

Cite this

Mimilakis, S. I., Bryan, N. J., & Smaragdis, P. (2020). One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 256-260). [9054108] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054108

One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization. / Mimilakis, Stylianos I.; Bryan, Nicholas J.; Smaragdis, Paris.

2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 256-260 9054108 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mimilakis, SI, Bryan, NJ & Smaragdis, P 2020, One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization. in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings., 9054108, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, Institute of Electrical and Electronics Engineers Inc., pp. 256-260, 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, Barcelona, Spain, 5/4/20. https://doi.org/10.1109/ICASSP40776.2020.9054108

Mimilakis SI, Bryan NJ, Smaragdis P. One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 256-260. 9054108. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP40776.2020.9054108

Mimilakis, Stylianos I. ; Bryan, Nicholas J. ; Smaragdis, Paris. / One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization. 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 256-260 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{443df340210f49a89bc7d2c84e4dfa07,

title = "One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization",

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.",

keywords = "Parametric style transfer, deep learning, one-shot learning, parametric equalization",

author = "Mimilakis, {Stylianos I.} and Bryan, {Nicholas J.} and Paris Smaragdis",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",

year = "2020",

month = may,

doi = "10.1109/ICASSP40776.2020.9054108",

language = "English (US)",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "256--260",

booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",

address = "United States",

}

TY - GEN

T1 - One-Shot Parametric Audio Production Style Transfer with Application to Frequency Equalization

AU - Mimilakis, Stylianos I.

AU - Bryan, Nicholas J.

AU - Smaragdis, Paris

PY - 2020/5

Y1 - 2020/5

N2 - 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.

AB - 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.

KW - Parametric style transfer

KW - deep learning

KW - one-shot learning

KW - parametric equalization

UR - http://www.scopus.com/inward/record.url?scp=85089229888&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85089229888&partnerID=8YFLogxK

U2 - 10.1109/ICASSP40776.2020.9054108

DO - 10.1109/ICASSP40776.2020.9054108

M3 - Conference contribution

AN - SCOPUS:85089229888

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 256

EP - 260

BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020

Y2 - 4 May 2020 through 8 May 2020

ER -