Abstract
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.
Original language | English |
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Pages (from-to) | 293-296 |
Number of pages | 4 |
Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
State | Published - 2011 |
Event | 12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy Duration: 27 Aug 2011 → 31 Aug 2011 |
Keywords
- Acoustic event classification
- Hidden Markov models
- Model adaptation