Detection and Classification of Acoustic Scenes and Events

Dan Stowell (1) , Dimitrios Giannoulis (1) , Emmanouil Benetos (2) , Mathieu Lagrange (3) , Mark D. Plumbley (1)

1 Centre for Digital Music
2 QMUL - Queen Mary University of London
3 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes

—For intelligent systems to make best use of the audio modality, it is important that they can recognize not just speech and music, which have been researched as specific tasks, but also general sounds in everyday environments. To stimulate research in this field we conducted a public research challenge: the IEEE Audio and Acoustic Signal Processing Technical Committee challenge on Detection and Classification of Acoustic Scenes and Events (DCASE). In this paper, we report on the state of the art in automatically classifying audio scenes, and automatically detecting and classifying audio events. We survey prior work as well as the state of the art represented by the submissions to the challenge from various research groups. We also provide detail on the organization of the challenge, so that our experience as challenge hosts may be useful to those organizing challenges in similar domains. We created new audio datasets and baseline systems for the challenge; these, as well as some submitted systems, are publicly available under open licenses, to serve as benchmarks for further research in general-purpose machine listening.

Mots clés: pattern recognition; machine intelligence; event detection; Index Terms—Audio databases

Domaines: Machine Learning [stat.ML]

Dan Stowell, Dimitrios Giannoulis, Emmanouil Benetos, Mathieu Lagrange, Mark D. Plumbley. Detection and Classification of Acoustic Scenes and Events. IEEE Transactions on Multimedia, 2015, 17 (10), ⟨10.1109/TMM.2015.2428998⟩. ⟨hal-01253912⟩