Timing is a core expressive material of real-time media. Practitioners regularly need to generate periodic events at controlled paces such as blinking lights or rhythmic sounds. Since strict periodicity often feels artificial and machinic, a common approach consists in adding randomness to create a more organic, less predictable cadence. However, simple jittering approaches that inject noise directly into the period or frequency of a process provide limited control and can distort the expected timing. This report presents a method that overcomes these limitations by generating irregular but statistically reliable event sequences. Based on the Poisson distribution, it preserves the desired long-run event rate while allowing variability to be modulated precisely, yielding rhythms that feel natural without compromising timing accuracy. We compare several approaches and introduce a mixed Poisson model that offers a continuous, intuitive control over randomness, from stable metronome-like pacing to expressive, burst-like irregularity. Practical implementation on embedded systems with limited computational resources is also presented, demonstrating that the method is expressive and lightweight.
