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Differentiable Black-box and Gray-box Modeling of Nonlinear Audio Effects (2502.14405v1)

Published 20 Feb 2025 in cs.SD and eess.AS

Abstract: Audio effects are extensively used at every stage of audio and music content creation. The majority of differentiable audio effects modeling approaches fall into the black-box or gray-box paradigms; and most models have been proposed and applied to nonlinear effects like guitar amplifiers, overdrive, distortion, fuzz and compressor. Although a plethora of architectures have been introduced for the task at hand there is still lack of understanding on the state of the art, since most publications experiment with one type of nonlinear audio effect and a very small number of devices. In this work we aim to shed light on the audio effects modeling landscape by comparing black-box and gray-box architectures on a large number of nonlinear audio effects, identifying the most suitable for a wide range of devices. In the process, we also: introduce time-varying gray-box models and propose models for compressor, distortion and fuzz, publish a large dataset for audio effects research - ToneTwist AFx https://github.com/mcomunita/tonetwist-afx-dataset - that is also the first open to community contributions, evaluate models on a variety of metrics and conduct extensive subjective evaluation. Code https://github.com/mcomunita/nablafx and supplementary material https://github.com/mcomunita/nnlinafx-supp-material are also available.

Summary

  • The paper evaluates differentiable black-box and gray-box models for nonlinear audio effects, identifying structured state-state models (S4) as outperforming others across diverse devices.
  • Temporal conditioning like TFiLM enhances model performance, particularly for complex distortion effects, by capturing long-range audio signal dependencies.
  • The findings enable the development of highly accurate and efficient virtual audio effects for music production, creating realistic digital reproductions of analog hardware.

Differentiable Black-box and Gray-box Modeling of Nonlinear Audio Effects

The paper "Differentiable Black-box and Gray-box Modeling of Nonlinear Audio Effects" presents an in-depth comparative paper of modeling strategies for nonlinear audio effects, focusing on differentiable black-box and gray-box approaches. This research is particularly relevant due to the pivotal role that audio effects play in music production, heavily influencing the artistic outcomes at various stages of music creation.

Overview

In the paper, the authors tackle the long-standing challenges associated with accurately modeling nonlinear audio effects like guitar amplifiers, overdrive, distortion, fuzz, and compressors. Many existing models are typically tested on a limited spectrum of devices, making cross-device generalization an open research area. By evaluating a comprehensive set of nonlinear effects over a large number of devices, the authors aim to identify the most suitable modeling techniques that exhibit both high fidelity and computational efficiency.

Methodology

The authors explore a variety of black-box and gray-box models and propose new architectures within these paradigms:

  1. Black-box Models: These models include recurrent neural networks, temporal convolution networks (TCN), gated convolution networks (GCN), and structured state-space sequence models (S4). Each of these models is tested with and without conditioning mechanisms like TFiLM, which insert time-varying feature modulation to improve expressivity.
  2. Gray-box Models: Time-varying gray-box models that combine parameter estimation and signal chain structures are introduced. These models are designed to use partial theoretical structures paired with input-output data to predict the behavior of the audio effects more accurately.

Furthermore, a large dataset named ToneTwist AFx is introduced, providing a comprehensive collection of dry-input/wet-output signal pairs for various audio effects. This dataset is essential for training and evaluating the proposed models across diverse devices and settings.

Results

  • The paper highlights that while both black-box and gray-box models have their merits, structured state-space models (S4, particularly with TFiLM) consistently outperform other architectures across a wide range of devices, demonstrating superior performance in capturing complex audio effects.
  • Temporal conditioning mechanisms like TFiLM enhance the performance by more effectively capturing long-range dependencies critical in audio effects processing, proving especially beneficial for high-distortion effects like fuzz and distortion.
  • In terms of computational efficiency, convolutional backbones are favored due to their scalable architecture, which can be optimized to achieve real-time processing capabilities. S4 models, though best in performance, require careful design choices to ensure they are computationally feasible for real-time applications.

Implications and Future Work

The research supports practical implementations of audio effects modeling in music production, offering models that are both accurate and efficient enough for real-time applications. This opens the door to more sophisticated virtual reproductions of analog effects in digital environments, reducing the need for physical hardware while maintaining sound quality. Moreover, the insights gained from this paper can lead to advancements in AI-driven dynamic audio processing, facilitating new creative possibilities for music producers and sound engineers.

Future research could focus on:

  • Enhancing gray-box models to achieve performance parity with black-box models.
  • Developing improved objective metrics to better correlate with perceptual evaluations of audio quality.
  • Extending the framework to encompass a wider range of audio effects, including linear and modulation types.
  • Investigating pruning and distillation techniques to optimize models for reduced computational demands without compromising accuracy.

Conclusion

This paper makes a significant contribution to the field of audio effects modeling by systematically exploring and comparing differentiable modeling techniques. The introduction of comprehensive datasets and a robust evaluation framework ensures that the research findings are well-grounded and applicable to real-world scenarios. By identifying key modeling strategies and computational frameworks, it sets the stage for future innovations in both the academic paper and practical implementation of advanced audio effects processing.

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