Healthcare audio event classification using hidden Markov models and hierarchical hidden Markov models

Authors:
Ya-Ti Peng;Ching-Yung Lin;Ming-Ting Sun;Kun-Cheng Tsai
Affiliations:
Department of Electrical Engineering, University of Washington, Seattle, WA;IBM T. J. Watson Research Center, Hawthorne, NY;Department of Electrical Engineering, University of Washington, Seattle, WA;Institute of Information Industry, Taipei, Taiwan
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 7
Cited 0

Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization

Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
The Hierarchical Hidden Markov Model: Analysis and Applications

Machine Learning
Acoustic environment classification

ACM Transactions on Speech and Language Processing (TSLP)
Semantic context detection using audio event fusion: camera-ready version

EURASIP Journal on Applied Signal Processing
Pseudo pitch synchronous analysis of speech with applications to speaker recognition

IEEE Transactions on Audio, Speech, and Language Processing
Information extraction from sound for medical telemonitoring

IEEE Transactions on Information Technology in Biomedicine
Content-based audio classification and retrieval by support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Audio is a useful modality complement to video for healthcare monitoring. In this paper, we investigate the use of Hierarchical Hidden Markov Models (HHMMs) for healthcare audio event classification. We show that HHMM can handle audio events with recursive patterns to improve the classification performance. We also propose a model fusion method to cover large variations often existing in healthcare audio events. Experimental results from classifying key eldercare audio events show the effectiveness of the model fusion method for healthcare audio event classification.