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：模拟电子电路构成了一类重要音乐设备的核心。其电子元件的非线性特性赋予模拟音乐设备独特的音色与音质，使其备受青睐。人工神经网络，特别是循环网络，在模拟音频效果电路仿真领域迅速普及。尽管神经网络方法已成功实现失真电路的精确建模，但仍需在架构层面改进以兼顾参数调节与低延迟响应。本文探索了近期机器学习进展在虚拟模拟建模中的应用。我们将状态空间模型和线性循环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处理信号历史编码的显著提升能力。本比较研究采用多种音频效果，运用上述黑箱神经建模技术，通过多维度指标评估模型在精确复现音频信号能量包络、频率成分及瞬态特征方面的性能与局限。为整合控制参数，我们采用特征级线性调制方法。实验表明：长短期记忆网络在失真与均衡器仿真中精度更优；而状态空间模型（及在编码器-解码器结构中集成的长短期记忆网络）在饱和与压缩效果仿真中表现最佳。针对长时变特性，状态空间模型展现出最高的建模精度。长短期记忆网络，尤其是线性循环单元网络，则更易引入音频伪影。