Improving Sound Event Detection Metrics: Insights from DCASE 2020

Giacomo Ferroni (1) , Nicolas Turpault (2) , Juan Azcarreta (1) , Francesco Tuveri (1) , Romain Serizel (2) , Çagdaş Bilen (1) , Sacha Krstulović (1)

1 Audio Analytic
2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication

The ranking of sound event detection (SED) systems may be biased by assumptions inherent to evaluation criteria and to the choice of an operating point. This paper compares conventional event-based and segment-based criteria against the Polyphonic Sound Detection Score (PSDS)'s intersection-based criterion, over a selection of systems from DCASE 2020 Challenge Task 4. It shows that, by relying on collars , the conventional event-based criterion introduces different strictness levels depending on the length of the sound events, and that the segment-based criterion may lack precision and be application dependent. Alternatively, PSDS's intersection-based criterion overcomes the dependency of the evaluation on sound event duration and provides robustness to labelling subjectivity, by allowing valid detections of interrupted events. Furthermore, PSDS enhances the comparison of SED systems by measuring sound event modelling performance independently from the systems' operating points.

Mots clés: sound event detection; evaluation metrics; segment vs event criteria; polyphonic sound detection score

Domaines: Intelligence artificielle [cs.AI]; Traitement du signal et de l'image [eess.SP]; Son [cs.SD]

Giacomo Ferroni, Nicolas Turpault, Juan Azcarreta, Francesco Tuveri, Romain Serizel, et al.. Improving Sound Event Detection Metrics: Insights from DCASE 2020. ICASSP 2021 - 46th International Conference on Acoustics, Speech, and Signal Processing, Jun 2021, Toronto/Virtual, Canada. ⟨10.1109/ICASSP39728.2021.9414711⟩. ⟨hal-02978422⟩