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.

通过比较State Space模型、Linear Recurrent Units和Long Short Term Memory网络在模拟音频效果方面的性能，本文研究了近期机器学习进展在虚拟类比建模中的应用，包括信号历史编码、能量包络、频率内容和瞬态等方面的准确度。结果表明，Long Short Term Memory网络在模拟失真和均衡器方面的准确度较高，而State Space模型在饱和和压缩方面的模拟能力超过其他方法。对于长时间变化特性，State Space模型展现了最高的准确度。Long Short Term Memory网络和Linear Recurrent Unit网络则更容易引入音频伪像。

虚拟模拟音频效果建模的循环神经网络比较研究