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

Mathieu Lagrange (1) , Grégoire Lafay (1) , Mathias Rossignol (2) , Emmanouil Benetos (3) , Axel Roebel (2)

1 ADTSI
2 Analyse et synthèse sonores [Paris]
3 QMUL - Queen Mary University of London

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.

Mots clés: experimental validation; auditory scene analysis; acoustic event detection

Domaines: Machine Learning [stat.ML]; Traitement du signal et de l'image [eess.SP]

Mathieu Lagrange, Grégoire Lafay, Mathias Rossignol, Emmanouil Benetos, Axel Roebel. An evaluation framework for event detection using a morphological model of acoustic scenes. 2015. ⟨hal-01111381v1⟩