  Singing voice beat and downbeat tracking posses several applications in
automatic music production, analysis and manipulation. Among them, some require
real-time processing, such as live performance processing and
auto-accompaniment for singing inputs. This task is challenging owing to the
non-trivial rhythmic and harmonic patterns in singing signals. For real-time
processing, it introduces further constraints such as inaccessibility to future
data and the impossibility to correct the previous results that are
inconsistent with the latter ones. In this paper, we introduce the first system
that tracks the beats and downbeats of singing voices in real-time.
Specifically, we propose a novel dynamic particle filtering approach that
incorporates offline historical data to correct the online inference by using a
variable number of particles. We evaluate the performance on two datasets:
GTZAN with the separated vocal tracks, and an in-house dataset with the
original vocal stems. Experimental result demonstrates that our proposed
approach outperforms the baseline by 3-5%.
