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Open AccessArticle

Deep Learning for Black-Box Modeling of Audio Effects

Centre for Digital Music, Queen Mary University of London, Mile End Road, London E1 4NS, UK
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This paper is an extended version of our paper published in the International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, 4–7 September 2019.
Appl. Sci. 2020, 10(2), 638; https://doi.org/10.3390/app10020638
Received: 29 November 2019 / Revised: 23 December 2019 / Accepted: 13 January 2020 / Published: 16 January 2020
(This article belongs to the Special Issue Digital Audio Effects)
Virtual analog modeling of audio effects consists of emulating the sound of an audio processor reference device. This digital simulation is normally done by designing mathematical models of these systems. It is often difficult because it seeks to accurately model all components within the effect unit, which usually contains various nonlinearities and time-varying components. Most existing methods for audio effects modeling are either simplified or optimized to a very specific circuit or type of audio effect and cannot be efficiently translated to other types of audio effects. Recently, deep neural networks have been explored as black-box modeling strategies to solve this task, i.e., by using only input–output measurements. We analyse different state-of-the-art deep learning models based on convolutional and recurrent neural networks, feedforward WaveNet architectures and we also introduce a new model based on the combination of the aforementioned models. Through objective perceptual-based metrics and subjective listening tests we explore the performance of these models when modeling various analog audio effects. Thus, we show virtual analog models of nonlinear effects, such as a tube preamplifier; nonlinear effects with memory, such as a transistor-based limiter and nonlinear time-varying effects, such as the rotating horn and rotating woofer of a Leslie speaker cabinet. View Full-Text
Keywords: black-box modeling; nonlinear; time-varying; audio effects; deep learning; tube amplifier; transistor-based limiter; Leslie speaker black-box modeling; nonlinear; time-varying; audio effects; deep learning; tube amplifier; transistor-based limiter; Leslie speaker
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Martínez Ramírez, M.A.; Benetos, E.; Reiss, J.D. Deep Learning for Black-Box Modeling of Audio Effects. Appl. Sci. 2020, 10, 638.

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