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Sound event detection (SED) entails identifying the type of sound andestimating its temporal boundaries from acoustic signals. These events areuniquely characterized by their spatio-temporal features, which are determinedby the way they are produced. In this study, we leverage some distinctivehigh-level acoustic characteristics of various sound events to assist the SEDmodel training, without requiring additional labeled data. Specifically, we usethe DCASE Task 4 2022 dataset and categorize the 10 classes into foursubcategories based on their high-level acoustic characteristics. We thenintroduce a novel multi-task learning framework that jointly trains the SED andhigh-level acoustic characteristics classification tasks, using shared layersand weighted loss. Our method significantly improves the performance of the SEDsystem, achieving a 36.3% improvement in terms of the polyphonic sound eventdetection score compared to the baseline on the DCASE 2022 Task 4 validationset.

A Multi-Task Learning Framework for Sound Event Detection using High-level Acoustic Characteristics of Sounds