# Polyphonic Sound Event and Sound Activity Detection: A Multi-task approach

## TL;DR

This paper introduces a multi-task learning approach for polyphonic sound event detection that jointly models event presence and activity to improve temporal localization and reduce errors in real-world audio recordings.

## Contribution

The paper proposes a novel joint multi-task model for sound event detection and activity detection, enhancing temporal localization and detection accuracy over separate models.

## Key findings

- Improved segment-wise detection metrics.
- Reduced false positives and false negatives.
- Enhanced event-wise detection performance.

## Abstract

Polyphonic Sound Event Detection (SED) in real-world recordings is a challenging task because of the dynamic polyphony level, intensity, and duration of sound events. Current polyphonic SED systems fail to model the temporal structure of sound events explicitly and instead attempt to look at which sound events are present at each audio frame. Consequently, the event-wise detection performance is much lower than the segment-wise detection performance. In this work, we propose a joint model approach to improve the temporal localization of sound events using a multi-task learning setup. The first task predicts which sound events are present at each time frame; we call this branch 'Sound Event Detection (SED) model', while the second task predicts if a sound event is present or not at each frame; we call this branch 'Sound Activity Detection (SAD) model'. We verify the proposed joint model by comparing it with a separate implementation of both tasks aggregated together from individual task predictions. Our experiments on the URBAN-SED dataset show that the proposed joint model can alleviate False Positive (FP) and False Negative (FN) errors and improve both the segment-wise and the event-wise metrics.