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Modeling nonlinear audio effects with end-to-end deep neural networks

Abstract: 

Audio processors whose parameters are modified periodically
over time are often referred as time-varying or modulation based
audio effects. Most existing methods for modeling these type of
effect units are often optimized to a very specific circuit and cannot
be efficiently generalized to other time-varying effects. Based on
convolutional and recurrent neural networks, we propose a deep
learning architecture for generic black-box modeling of audio processors
with long-term memory. We explore the capabilities of
deep neural networks to learn such long temporal dependencies
and we show the network modeling various linear and nonlinear,
time-varying and time-invariant audio effects. In order to measure
the performance of the model, we propose an objective metric
based on the psychoacoustics of modulation frequency perception.
We also analyze what the model is actually learning and how the
given task is accomplished.

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Paper Details

Authors:
Emmanouil Benetos, Joshua D. Reiss
Submitted On:
10 May 2019 - 12:06pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Marco A. Martinez Ramirez
Paper Code:
AASP-L6.5
Document Year:
2019
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Document Files

ICASSP___Presentation_Martinez_Ramirez.pdf

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[1] Emmanouil Benetos, Joshua D. Reiss, "Modeling nonlinear audio effects with end-to-end deep neural networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4368. Accessed: Sep. 20, 2019.
@article{4368-19,
url = {http://sigport.org/4368},
author = {Emmanouil Benetos; Joshua D. Reiss },
publisher = {IEEE SigPort},
title = {Modeling nonlinear audio effects with end-to-end deep neural networks},
year = {2019} }
TY - EJOUR
T1 - Modeling nonlinear audio effects with end-to-end deep neural networks
AU - Emmanouil Benetos; Joshua D. Reiss
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4368
ER -
Emmanouil Benetos, Joshua D. Reiss. (2019). Modeling nonlinear audio effects with end-to-end deep neural networks. IEEE SigPort. http://sigport.org/4368
Emmanouil Benetos, Joshua D. Reiss, 2019. Modeling nonlinear audio effects with end-to-end deep neural networks. Available at: http://sigport.org/4368.
Emmanouil Benetos, Joshua D. Reiss. (2019). "Modeling nonlinear audio effects with end-to-end deep neural networks." Web.
1. Emmanouil Benetos, Joshua D. Reiss. Modeling nonlinear audio effects with end-to-end deep neural networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4368