Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches

2025-04-07

AI Summary

Blind identification of nonlinear audio effects from unpaired input-output signals remains a challenging unsupervised learning problem. Method: This paper proposes an unsupervised probabilistic identification framework based on diffusion generative models—the first application of diffusion models to audio system identification. We conduct systematic comparisons with adversarial approaches, notably generative adversarial networks (GANs), revealing complementary strengths: diffusion models exhibit superior robustness to noise and data scarcity, along with improved training stability; GANs excel at modeling strong nonlinear distortion characteristics. Contribution/Results: Evaluated on guitar distortion effects, our method significantly enhances the reliability and generalizability of blind identification. It establishes a novel paradigm for music information retrieval and intelligent audio processing, advancing the state of the art in unsupervised nonlinear system identification.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Accurately estimating nonlinear audio effects without access to paired input-output signals remains a challenging problem.This work studies unsupervised probabilistic approaches for solving this task. We introduce a method, novel for this application, based on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using black- and gray-box models. This study compares this method with a previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the effect operator and varying lengths of available effected recordings.Through experiments on guitar distortion effects, we show that the diffusion-based approach provides more stable results and is less sensitive to data availability, while the adversarial approach is superior at estimating more pronounced distortion effects. Our findings contribute to the robust unsupervised blind estimation of audio effects, demonstrating the potential of diffusion models for system identification in music technology.

Problem

• Estimating nonlinear audio effects without paired signals
• Comparing diffusion and adversarial unsupervised methods
• Evaluating performance under varying effect parameters

Innovation

• Diffusion models for blind system identification
• Comparison with adversarial approach performance
• Robust unsupervised audio effects estimation