Machine learning models have become ubiquitous in modeling
analog audio devices. Expanding on this line of research, our study
focuses on Voltage-Controlled Oscillators of analog synthesizers.
We employ black box autoregressive artificial neural networks to
model the typical analog waveshapes, including triangle, square,
and sawtooth. The models can be conditioned on wave frequency
and type, enabling the generation of pitch envelopes and morphing across waveshapes. We conduct evaluations on both synthetic
and analog datasets to assess the accuracy of various architectural
variants. The LSTM variant performed better, although lower frequency ranges present particular challenges.