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

Impact of Sound Duration and Inactive Frames on Sound Event Detection Performance