Browse > Article
http://dx.doi.org/10.5392/JKCA.2010.10.7.031

Improvement of Environmental Sounds Recognition by Post Processing  

Park, Jun-Qyu (전남대학교 전자컴퓨터공학부)
Baek, Seong-Joon (전남대학교 전자컴퓨터공학부)
Publication Information
The Journal of the Korea Contents Association / v.10, no.7, 2010 , pp. 31-39 More about this Journal
Abstract
In this study, we prepared the real environmental sound data sets arising from people's movement comprising 9 different environment types. The environmental sounds are pre-processed with pre-emphasis and Hamming window, then go into the classification experiments with the extracted features using MFCC (Mel-Frequency Cepstral Coefficients). The GMM (Gaussian Mixture Model) classifier without post processing tends to yield abruptly changing classification results since it does not consider the results of the neighboring frames. Hence we proposed the post processing methods which suppress abruptly changing classification results by taking the probability or the rank of the neighboring frames into account. According to the experimental results, the method using the probability of neighboring frames improve the recognition performance by more than 10% when compared with the method without post processing.
Keywords
Environmental Sound; GMM; Post-processing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 L. Ma, B. P. Milner, and D. Smith, “Acoustic environment classification,” ACM Transactions on Speech and Language Processing, Vol.3, No.2, pp.1-22. 2006.   DOI
2 Y. Toyoda, J. Huang, S. Ding, and Y. Liu, “Environmental sound recognition by multilayered neural networks,” International Conference on Computer and Information Technology, pp.123-127, 2004.   DOI
3 L. Couvreur and M. Laniray, “Automatic noise recognition in urban environments based on artificial neural networks and hidden Markov models,” InterNoise, Prague, Czech Republic, pp.1-8. 2004.
4 N. Sawhney, “Situational awareness from environmental sounds,” MIT Media Lab. Technical Report, 1997.
5 S. Chu, S. Narayana, C.-C. J. Kuo, and M. J. Mataric, "Where am I? Scene recognition for mobile robots using audio features," in Proc. ICME, 2006.   DOI
6 R. G. Malkin and A. Waibel, "Classifying user environment for mobile applications using linear autoencoding of ambient audio," in Proc. ICASSP, 2005.   DOI
7 A. Eronen, V. Peltonen, J. Tuomi, A. Klapuri, S. Fagerlund, T. Sorsa, G. Lorho, and J. Huopaniemi, "Audio-based context recognition,“ IEEE Trans. on Audio, Speech, and Language Processing, Vol.14, No.1, pp.321-329, 2006(1).   DOI   ScienceOn
8 S. Chu, S. Narayanan, and C.-C. Jay Kuo “Environmental Sound Recognition With Time-Frequency Audio Features,” IEEE Trans. on Audio, Speech, and Language Processing, Vol.17, No.6, pp.1-16, 2009.   DOI   ScienceOn
9 C. M. Bishop, Neural networks for pattern recognition, Oxford University Press, UK, 1995.
10 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classfication, Jone Wiley & Son, Inc. 2001.
11 http://www.teco.edu/tea/