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
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
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 -