用五种神经网络模拟弹簧混响，提升数字音频效果的逼真度。
Evaluating Neural Networks Architectures for Spring Reverb Modelling
- 对比卷积与循环神经网络在混响建模中的表现
- 在16kHz和48kHz采样率下验证模型效果
- 适合对音频建模和黑箱技术感兴趣的开发者
混响是空间音频感知的关键元素，传统上通过模拟设备（如板式和弹簧混响）实现，近几十年来则借助数字信号处理技术发展出多种虚拟模拟建模（VAM）方法。弹簧混响的机电工作机制使其成为非线性系统，难以通过白箱建模技术在数字域中完全复现。本研究比较了五种不同的神经网络架构，包括卷积与循环模型，评估其在复制该音频效果特征方面的有效性。实验基于两个数据集，在16kHz和48kHz采样率下进行。本文特别关注具备参数控制能力的神经音频架构，旨在推动当前黑箱建模技术在弹簧混响领域的边界。
原文摘要 · Abstract (English)
Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM). The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain with white-box modelling techniques. In this study, we compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. The evaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.
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