Acoustic Event Classification Using Ensemble of One-Class Classifiers for Monitoring Application

In this paper we investigate the application of ensemble of one-class classifiers to the problem of acoustic event classification. We present some initial results that are based on acoustic signals emitted by different litter causing material when contacted by human. When a person interacts with objects made with specific material, characteristic sounds are produced as a result of the interactions. We consider such interactions or activities as atomic events. We propose application of ensemble of one-class fuzzy rule-based classifiers to the problem of identification of activities that can cause possible litter in the public places. The experimental results show that the classifier gives satisfactory results and at the same time has low false alarm rate. The results are comparable to widely used one-class SVM. Moreover, the method is adaptive and suitable for incremental learning.

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

[2]  Amir Shirkhodaie,et al.  A survey on acoustic signature recognition and classification techniques for persistent surveillance systems , 2012, Defense + Commercial Sensing.

[3]  Ming Liu,et al.  HMM-Based Acoustic Event Detection with AdaBoost Feature Selection , 2007, CLEAR.

[4]  Goutam Saha,et al.  Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition , 2012, Speech Commun..

[5]  M. Popescu,et al.  Acoustic fall detection using one-class classifiers , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Ludmila I. Kuncheva,et al.  How good are fuzzy If-Then classifiers? , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[7]  T. Kuroda,et al.  Human Activity Recognition from Environmental Background Sounds for Wireless Sensor Networks , 2007, 2007 IEEE International Conference on Networking, Sensing and Control.

[8]  Eyke Hüllermeier,et al.  FR3: A Fuzzy Rule Learner for Inducing Reliable Classifiers , 2009, IEEE Transactions on Fuzzy Systems.

[9]  Okure U Obot,et al.  Experimental study of fuzzy-rule based management of tropical diseases: case of malaria diagnosis. , 2008, Studies in health technology and informatics.

[10]  Hisao Ishibuchi,et al.  Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[11]  SahaGoutam,et al.  Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition , 2012 .

[12]  Detlef Nauck,et al.  Foundations Of Neuro-Fuzzy Systems , 1997 .

[13]  Bai Liang,et al.  Feature analysis and extraction for audio automatic classification , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[14]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[15]  Lie Lu,et al.  Content analysis for audio classification and segmentation , 2002, IEEE Trans. Speech Audio Process..

[16]  Kai Oliver Arras,et al.  Audio-based human activity recognition using Non-Markovian Ensemble Voting , 2012, 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication.

[17]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[18]  Mark Hasegawa-Johnson,et al.  Acoustic fall detection using Gaussian mixture models and GMM supervectors , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[19]  Mihail Popescu,et al.  A Fuzzy Logic System for Acoustic Fall Detection , 2008, AAAI Fall Symposium: AI in Eldercare: New Solutions to Old Problems.

[20]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[21]  Andrey Temko,et al.  Classification of acoustic events using SVM-based clustering schemes , 2006, Pattern Recognit..

[22]  Pei Zhang,et al.  CoughLoc: Location-Aware Indoor Acoustic Sensing for Non-Intrusive Cough Detection , 2011 .

[23]  Thomas S. Huang,et al.  Feature analysis and selection for acoustic event detection , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[24]  Robert P. W. Duin,et al.  Combining One-Class Classifiers , 2001, Multiple Classifier Systems.

[25]  Guodong Guo,et al.  Content-based audio classification and retrieval by support vector machines , 2003, IEEE Trans. Neural Networks.

[26]  Thomas S. Huang,et al.  Real-world acoustic event detection , 2010, Pattern Recognit. Lett..