Learning New Acoustic Events in an HMM-Based System Using MAP Adaptation

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.

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