  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.
