An evaluation framework for event detection using a morphological model of acoustic scenes

TL;DR

This paper presents a morphological acoustic scene model to evaluate event detection systems, enabling analysis of scene structure impacts on system robustness and guiding future improvements.

Contribution

It introduces a novel morphological modeling approach for acoustic scenes to evaluate detection systems under varied conditions.

Findings

Model effectively isolates scene structure effects on detection performance

System robustness improves with background sound management

Model validated using IEEE DCASE Challenge data

Abstract

This paper introduces a model of environmental acoustic scenes which adopts a morphological approach by ab-stracting temporal structures of acoustic scenes. To demonstrate its potential, this model is employed to evaluate the performance of a large set of acoustic events detection systems. This model allows us to explicitly control key morphological aspects of the acoustic scene and isolate their impact on the performance of the system under evaluation. Thus, more information can be gained on the behavior of evaluated systems, providing guidance for further improvements. The proposed model is validated using submitted systems from the IEEE DCASE Challenge; results indicate that the proposed scheme is able to successfully build datasets useful for evaluating some aspects the performance of event detection systems, more particularly their robustness to new listening conditions and the increasing level of background sounds.