In this paper, we present a system for the recognition of acoustic events suited for a robotic application. HMMs are used to model different acoustic event classes. We are especially looking at the open-set case, where a class of acoustic events occurs that was not included in the training phase. It is evaluated how newly occuring classes can be learnt using MAP adaptation or conventional training methods. A small database of acoustic events was recorded with a robotic platform to perform the experiments.
Bibliographic reference. Geiger, Jürgen T. / Lakhal, Mohamed Anouar / Schuller, Björn / Rigoll, Gerhard (2011): "Learning new acoustic events in an HMM-based system using MAP adaptation", In INTERSPEECH-2011, 293-296.