Blind Estimation of Audio Processing Graph

Lee, Sungho; Park, Jaehyun; Paik, Seungryeol; Lee, Kyogu

doi:10.1109/icassp49357.2023.10096581

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…6. Directed acyclic audio signal processing graphs have been estimated blindly (Lee et al, 2023), while FM routing has been optimised through neural architecture search (Ye et al, 2023). Can these methods be generalised to allow estimation of arbitrary synthesiser topologies?…”

Section: Open Questionsmentioning

confidence: 99%

A review of differentiable digital signal processing for music and speech synthesis

Hayes,

Shier,

Fazekas

et al. 2024

Front. Signal Process.

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The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research.

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Section: Open Questionsmentioning

confidence: 99%

A review of differentiable digital signal processing for music and speech synthesis

Hayes,

Shier,

Fazekas

et al. 2024

Front. Signal Process.

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Blind Estimation of Audio Effects Using an Auto-Encoder Approach and Differentiable Digital Signal Processing

Peladeau,

Peeters

2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Blind Estimation of Audio Effects (BE-AFX) aims at estimating the audio effects (AFXs) applied to an original, unprocessed audio sample solely based on the processed audio sample. To train such a system traditional approaches optimize a loss between ground truth and estimated AFX parameters. This involves knowing the exact implementation of the AFXs used for the process. In this work, we propose an alternative solution that eliminates the requirement for knowing this implementation. Instead, we introduce an auto-encoder approach, which optimizes an audio quality metric. We explore, suggest, and compare various implementations of commonly used mastering AFXs, using differential signal processing or neural approximations. Our findings demonstrate that our auto-encoder approach yields superior estimates of the audio quality produced by a chain of AFXs, compared to the traditional parameter-based approach, even if the latter provides a more accurate parameter estimation.

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Blind extraction of guitar effects through blind system inversion and neural guitar effect modeling

Hinrichs,

Gerkens,

Lange

et al. 2024

J AUDIO SPEECH MUSIC PROC.

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Audio effects are an ubiquitous tool in music production due to the interesting ways in which they can shape the sound of music. Guitar effects, the subset of all audio effects focusing on guitar signals, are commonly used in popular music to shape the guitar sound to fit specific genres or to create more variety within musical compositions. Automatic extraction of guitar effects and their parameter settings, with the aim to copy a target guitar sound, has been previously investigated, where artificial neural networks first determine the effect class of a reference signal and subsequently the parameter settings. These approaches require a corresponding guitar effect implementation to be available. In general, for very close sound matching, additional research regarding effect implementations is necessary. In this work, we present a different approach to circumvent these issues. We propose blind extraction of guitar effects through a combination of blind system inversion and neural guitar effect modeling. That way, an immediately usable, blind copy of the target guitar effect is obtained. The proposed method is tested with the phaser, softclipping and slapback delay effect. Listening tests with eight subjects indicate excellent quality of the blind copies, i.e., little to no difference to the reference guitar effect.

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Blind Estimation of Audio Processing Graph

Cited by 3 publications

References 15 publications

A review of differentiable digital signal processing for music and speech synthesis

A review of differentiable digital signal processing for music and speech synthesis

Blind Estimation of Audio Effects Using an Auto-Encoder Approach and Differentiable Digital Signal Processing

Blind extraction of guitar effects through blind system inversion and neural guitar effect modeling

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