Analog electronic circuits are at the core of an important category of musical devices. The nonlinear features of their electronic components give analog musical devices a distinctive timbre and sound quality, making them highly desirable. Artificial neural networks have rapidly gained popularity for the emulation of analog audio effects circuits, particularly recurrent networks. While neural approaches have been successful in accurately modeling distortion circuits, they require architectural improvements that account for parameter conditioning and low latency response. In this article, we explore the application of recent machine learning advancements for virtual analog modeling. We compare State Space models and Linear Recurrent Units against the more common Long Short Term Memory networks. These have shown promising ability in sequence to sequence modeling tasks, showing a notable improvement in signal history encoding. Our comparative study uses these black box neural modeling techniques with a variety of audio effects. We evaluate the performance and limitations using multiple metrics aiming to assess the models' ability to accurately replicate energy envelopes, frequency contents, and transients in the audio signal. To incorporate control parameters we employ the Feature wise Linear Modulation method. Long Short Term Memory networks exhibit better accuracy in emulating distortions and equalizers, while the State Space model, followed by Long Short Term Memory networks when integrated in an encoder decoder structure, outperforms others in emulating saturation and compression. When considering long time variant characteristics, the State Space model demonstrates the greatest accuracy. The Long Short Term Memory and, in particular, Linear Recurrent Unit networks present more tendency to introduce audio artifacts.

翻译：模拟电子电路是重要音乐设备类别的核心。其电子元件的非线性特性赋予了模拟音乐设备独特的音色和声音质量，使其备受青睐。人工神经网络，尤其是递归网络，在模拟音频效果电路仿真领域迅速普及。尽管神经方法在精确建模失真电路方面取得了成功，但仍需考虑参数调节和低延迟响应的架构改进。本文探讨了最新机器学习进展在虚拟模拟建模中的应用，将状态空间模型和线性递归单元与更常见的长短期记忆网络进行对比。这些模型在序列到序列建模任务中展现出显著能力，在信号历史编码方面实现了显著改进。我们的对比研究采用这些黑箱神经建模技术处理多种音频效果，通过多个指标评估模型准确再现音频信号能量包络、频率内容及瞬态特性的性能与局限性。为融入控制参数，我们采用特征线性调制方法。实验表明：长短期记忆网络在仿真正弦波失真和均衡器方面具有更高精度；而状态空间模型及其集成于编码器-解码器结构后的长短期记忆网络，在仿真饱和与压缩效果时优于其他模型；在考虑长时间变化特性时，状态空间模型表现出最高精度。长短期记忆网络（尤其是线性递归单元网络）更容易引入音频伪影。