  Deep learning models have seen widespread use in modelling LFO-driven audio
effects, such as phaser and flanger. Although existing neural architectures
exhibit high-quality emulation of individual effects, they do not possess the
capability to manipulate the output via control parameters. To address this
issue, we introduce Controllable Neural Frame-based Modulation Effects
(CONMOD), a single black-box model which emulates various LFO-driven effects in
a frame-wise manner, offering control over LFO frequency and feedback
parameters. Additionally, the model is capable of learning the continuous
embedding space of two distinct phaser effects, enabling us to steer between
effects and achieve creative outputs. Our model outperforms previous work while
possessing both controllability and universality, presenting opportunities to
enhance creativity in modern LFO-driven audio effects.
