A MULTI-TASK LEARNING METHOD FOR WEAKLY SUPERVISED SOUND EVENT DETECTION

In weakly supervised sound event detection (SED), only coarse-grained labels are available, and thus the supervision information is quite limited. To fully utilize prior knowledge of the time-frequency masks of each sound event, we propose a novel multi-task learning (MTL) method that takes SED as the main task and source separation as the auxiliary task. For active events, we minimize the overlap of their masks as the segment loss to learn distinguishing features. For inactive events, the proposed method measures the activity of masks as silent loss to reduce the insertion error. The auxiliary source separation task calculates an extra penalty according to the shared masks, which can further incorporate prior knowledge in the form of regularization constraints. We demonstrated that the proposed method can effectively reduce the insertion error and achieve a better performance in SED task than single-task methods.

Automated summary

In weakly supervised sound event detection, a novel multi-task learning method is proposed to utilize the prior knowledge of time-frequency masks for each sound event. The method treats SED as the main task and source separation as the auxiliary task, and incorporates regularization constraints using shared masks.