HMM-based acoustic event detection with adaboost feature selection

Xi Zhou, Xiaodan Zhuang, Ming Liu, Hao Tang, Mark Hasegawa-Johnson, Thomas Huang

Research output: Contribution to journalConference articlepeer-review


Because of the spectral difference between speech and acous- tic events, we propose using Kullback-Leibler distance to quantify the discriminant capability of all speech feature components in acoustic event detection. Based on these distances, we use AdaBoost to select a discriminant feature set and demonstrate that this feature set outperforms classical speech feature set such as MFCC in one-pass HMM-based acoustic event detection. We implement an HMM-based acoustic events detection system with lattice rescoring using a feature set selected by the above AdaBoost based approach.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'HMM-based acoustic event detection with adaboost feature selection'. Together they form a unique fingerprint.

Cite this