  Strong labels are a necessity for evaluation of sound event detection
methods, but often scarcely available due to the high resources required by the
annotation task. We present a method for estimating strong labels using
crowdsourced weak labels, through a process that divides the annotation task
into simple unit tasks. Based on estimations of annotators' competence,
aggregation and processing of the weak labels results in a set of objective
strong labels. The experiment uses synthetic audio in order to verify the
quality of the resulting annotations through comparison with ground truth. The
proposed method produces labels with high precision, though not all event
instances are recalled. Detection metrics comparing the produced annotations
with the ground truth show 80% F-score in 1 s segments, and up to 89.5%
intersection-based F1-score calculated according to the polyphonic sound
detection score metrics.
