  Sound event detection (SED) entails identifying the type of sound and
estimating its temporal boundaries from acoustic signals. These events are
uniquely characterized by their spatio-temporal features, which are determined
by the way they are produced. In this study, we leverage some distinctive
high-level acoustic characteristics of various sound events to assist the SED
model training, without requiring additional labeled data. Specifically, we use
the DCASE Task 4 2022 dataset and categorize the 10 classes into four
subcategories based on their high-level acoustic characteristics. We then
introduce a novel multi-task learning framework that jointly trains the SED and
high-level acoustic characteristics classification tasks, using shared layers
and weighted loss. Our method significantly improves the performance of the SED
system, achieving a 36.3% improvement in terms of the polyphonic sound event
detection score compared to the baseline on the DCASE 2022 Task 4 validation
set.
