Audio Effect Estimation with DNN-Based Prediction and Search Algorithm

2026-04-24

AI Summary

This study addresses the inverse problem of estimating the types, ordering, and parameter configurations of audio effects applied to a processed (wet) signal. To this end, the authors propose a hybrid strategy that first employs a deep neural network to predict both the dry signal and the combination of effect types, followed by a signal-reconstruction-similarity-driven search algorithm to refine the effect sequence and parameters. This approach represents the first integration of deep neural network prediction with a reconstruction-guided search mechanism, further enhanced by explicit dry signal estimation to improve the accuracy of effect configuration recovery. Experimental results demonstrate that the proposed two-stage hybrid method significantly outperforms purely predictive approaches across multiple evaluation metrics, confirming its effectiveness and superiority in audio effect inversion.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Audio effects play an essential role in sound design. This research addresses the task of audio effect estimation, which aims to estimate the configuration of applied effects from a wet signal. Existing approaches to this problem can be categorized into predictive approaches, which use models pre-trained in a data-driven manner, and search-based approaches, which are based on wet signal reconstruction. In this study, we propose a novel approach that integrates these approaches: first, DNNs predict the dry signal and effect configuration, and then a search is performed based on wet signal reconstruction using these predictions. By estimating the dry signal in the prediction stage, it becomes possible to complement or improve the predictions using reconstruction similarity as an objective function. The experimental evaluation showed that methods based on the proposed approach outperformed the method solely based on the predictive approach. Furthermore, the findings suggest that the task division of predicting the effect type combination followed by the search-based estimation of order and parameters was the most effective across various metrics.

Problem

• audio effect estimation
• wet signal
• effect configuration
• sound design

Innovation

• audio effect estimation
• deep neural network
• signal reconstruction
• search-based optimization
• dry/wet signal separation