ISCA Archive Interspeech 2013
ISCA Archive Interspeech 2013

A blind segmentation approach to acoustic event detection based on i-vector

Zhen Huang, You-Chi Cheng, Kehuang Li, Ville Hautamäki, Chin-Hui Lee

We propose a new blind segmentation approach to acoustic event detection (AED) based on i-vectors. Conventional approaches to AED often required well-segmented data with non-overlapping boundaries for competing events. Inspired by block-based automatic image annotation in image retrieval tasks, we blindly segment audio streams into equal-length pieces, label the underlying observed acoustic events with multiple categories and with no event boundary information, extract i-vector for them, and perform classification using support vector machine and maximal figureof- merit based classifiers. Experiments on various sets of audio data show promising results with an average of 8% absolute gain in F1 over the conventional hidden Markov model based approach. An enhanced robustness at different noise levels is also observed. The key to the success lies in the enhanced discrimination power offered by the i-vector representation of the acoustic data.


doi: 10.21437/Interspeech.2013-535

Cite as: Huang, Z., Cheng, Y.-C., Li, K., Hautamäki, V., Lee, C.-H. (2013) A blind segmentation approach to acoustic event detection based on i-vector. Proc. Interspeech 2013, 2282-2286, doi: 10.21437/Interspeech.2013-535

@inproceedings{huang13_interspeech,
  author={Zhen Huang and You-Chi Cheng and Kehuang Li and Ville Hautamäki and Chin-Hui Lee},
  title={{A blind segmentation approach to acoustic event detection based on i-vector}},
  year=2013,
  booktitle={Proc. Interspeech 2013},
  pages={2282--2286},
  doi={10.21437/Interspeech.2013-535}
}