HMM-Based Acoustic Event Detection with AdaBoost Feature Selection

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.

[1]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[2]  Chloé Clavel,et al.  Events Detection for an Audio-Based Surveillance System , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[3]  Arnaud Martin,et al.  Voicing parameter and energy based speech/non-speech detection for speech recognition in adverse conditions , 2003, INTERSPEECH.

[4]  Rainer Stiefelhagen,et al.  Computers in the Human Interaction Loop , 2009, Human-Computer Interaction Series.

[5]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[6]  Lie Lu,et al.  Highlight sound effects detection in audio stream , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[7]  Mitch Weintraub,et al.  Using speech/non-speech detection to bias recognition search on noisy data , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[8]  John B. Moore,et al.  On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure , 1993, IEEE Trans. Signal Process..

[9]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[10]  Andrey Temko,et al.  ACOUSTIC EVENT DETECTION AND CLASSIFICATION IN SMART-ROOM ENVIRONMENTS: EVALUATION OF CHIL PROJECT SYSTEMS , 2006 .

[11]  Andrey Temko,et al.  Classification of meeting-room acoustic events with support vector machines and variable-feature-set clustering , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[12]  Alexander H. Waibel,et al.  Computers in the Human Interaction Loop , 2009, Handbook of Ambient Intelligence and Smart Environments.