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征赋予模拟音乐设备独特的音色和声音品质，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中迅速普及。尽管神经方法在失真电路精确建模方面取得成效，但仍需针对参数调节和低延迟响应进行架构改进。本文探索了近期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循环单元与更常用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卓越潜力，尤其体现在信号历史编码的显著改进上。我们的比较研究采用了这些黑箱神经建模技术，并针对多种音频效果进行评估。通过多个指标评估模型准确复制音频信号的能量包络、频率内容及瞬态特性的能力与局限。为纳入控制参数，我们采用了特征线性调制方法。长短期记忆网络在失真器和均衡器仿真中展现出更高精度，而状态空间模型以及采用编码器-解码器结构的长短期记忆网络，在饱和与压缩仿真中表现更优。在考虑长期时变特性时，状态空间模型精度最高。长短期记忆网络和线性循环单元网络更易引入音频伪影。