  In conventional sound event detection (SED) models, two types of events,
namely, those that are present and those that do not occur in an acoustic
scene, are regarded as the same type of events. The conventional SED methods
cannot effectively exploit the difference between the two types of events. All
time frames of sound events that do not occur in an acoustic scene are easily
regarded as inactive in the scene, that is, the events are easy-to-train. The
time frames of the events that are present in a scene must be classified as
active in addition to inactive in the acoustic scene, that is, the events are
difficult-to-train. To take advantage of the training difficulty, we apply
curriculum learning into SED, where models are trained from easy- to
difficult-to-train events. To utilize the curriculum learning, we propose a new
objective function for SED, wherein the events are trained from easy- to
difficult-to-train events. Experimental results show that the F-score of the
proposed method is improved by 10.09 percentage points compared with that of
the conventional binary cross entropy-based SED.
