AD-YOLO: You Look Only Once in Training Multiple Sound Event Localization and Detection

TL;DR

This paper introduces AD-YOLO, a novel sound event localization and detection model that effectively handles polyphony and unknown environments by adapting the YOLO framework with an angular-distance-based format.

Contribution

The paper presents AD-YOLO, a new SELD approach that improves generalization and polyphony handling through a location-sensitive format inspired by YOLO.

Findings

Achieved outstanding performance on DCASE datasets

Demonstrated robustness in class-homogeneous polyphony environments

Outperformed existing SELD methods in multiple metrics

Abstract

Sound event localization and detection (SELD) combines the identification of sound events with the corresponding directions of arrival (DOA). Recently, event-oriented track output formats have been adopted to solve this problem; however, they still have limited generalization toward real-world problems in an unknown polyphony environment. To address the issue, we proposed an angular-distance-based multiple SELD (AD-YOLO), which is an adaptation of the "You Only Look Once" algorithm for SELD. The AD-YOLO format allows the model to learn sound occurrences location-sensitively by assigning class responsibility to DOA predictions. Hence, the format enables the model to handle the polyphony problem, regardless of the number of sound overlaps. We evaluated AD-YOLO on DCASE 2020-2022 challenge Task 3 datasets using four SELD objective metrics. The experimental results show that AD-YOLO achieved outstanding performance overall and also accomplished robustness in class-homogeneous polyphony environments.