Learning new acoustic events in an HMM-based system using MAP adaptation
Jürgen T. Geiger, Mohamed Anouar Lakhal, Björn Wolfgang Schuller, Gerhard Rigoll · 2011
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.