  In many methods of sound event detection (SED), a segmented time frame is
regarded as one data sample to model training. The durations of sound events
greatly depend on the sound event class, e.g., the sound event "fan" has a long
duration, whereas the sound event "mouse clicking" is instantaneous. Thus, the
difference in the duration between sound event classes results in a serious
data imbalance in SED. Moreover, most sound events tend to occur occasionally;
therefore, there are many more inactive time frames of sound events than active
frames. This also causes a severe data imbalance between active and inactive
frames. In this paper, we investigate the impact of sound duration and inactive
frames on SED performance by introducing four loss functions, such as simple
reweighting loss, inverse frequency loss, asymmetric focal loss, and focal
batch Tversky loss. Then, we provide insights into how we tackle this imbalance
problem.
