Audio Effect Estimation with DNN-Based Prediction and Search Algorithm

TL;DR

This paper introduces a hybrid method combining DNN prediction and search algorithms to improve audio effect estimation from wet signals, outperforming purely predictive models.

Contribution

It presents a novel integrated approach that leverages deep neural networks and search algorithms for more accurate audio effect estimation.

Findings

Hybrid approach outperforms purely predictive methods.

Predicting effect type first, then search, yields best results.

Estimating dry signals enhances reconstruction-based accuracy.

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.