  Weakly-supervised learning has emerged as a promising approach to leverage
limited labeled data in various domains by bridging the gap between fully
supervised methods and unsupervised techniques. Acquisition of strong
annotations for detecting sound events is prohibitively expensive, making
weakly supervised learning a more cost-effective and broadly applicable
alternative. In order to enhance the recognition rate of the learning of
detection of weakly-supervised sound events, we introduce a Frame Pairwise
Distance (FPD) loss branch, complemented with a minimal amount of synthesized
data. The corresponding sampling and label processing strategies are also
proposed. Two distinct distance metrics are employed to evaluate the proposed
approach. Finally, the method is validated on the DCASE 2023 task4 dataset. The
obtained experimental results corroborated the efficacy of this approach.
