Sciweavers

Share
CLEAR
2007
Springer

HMM-Based Acoustic Event Detection with AdaBoost Feature Selection

11 years 11 months ago
HMM-Based Acoustic Event Detection with AdaBoost Feature Selection
Given the spectral difference between speech and acoustic 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.
Xi Zhou, Xiaodan Zhuang, Ming Liu, Hao Tang, Mark
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where CLEAR
Authors Xi Zhou, Xiaodan Zhuang, Ming Liu, Hao Tang, Mark Hasegawa-Johnson, Thomas S. Huang
Comments (0)
books