Environmental Sound Recognition Using Double-Level Energy Detection

Xiaoxia Zhang, Ying Li

College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China..

DOI: 10.4236/jsip.2013.43B004                

Abstract

The performance of classic Mel-frequency cepstral coefficients (MFCC) is unsatisfactory in noisy environment with different sound sources from nature. In this paper, a classification approach of the ecological environmental sounds using the double-level energy detection (DED) was presented. The DED was used to detect the existence of the sound signals under noise conditions. In addition, MFCC features from the frames which were detected the presence of the sound signals by DED were extracted. Experimental results show that the proposed technology has better noise immunity than classic MFCC, and also outperforms time-domain energy detection (TED) and frequency-domain energy detection (FED) respectively.

Keywords

Ecological Environmental Sounds; Double-Level Energy Detection; Time-Domain Energy Detection; Frequency-Domain Energy Detection; Mel-Frequency Cepstral Coefficients

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	R. Typke, F. Wiering and R. Veltkamp, “A Survey of Music Information Retrieval Systems,” Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, 11-15 September 2005, pp. 153-160.
[2]	L. Gerosa, G. Valenzise and M. Tagliasacchi, et al., “Scream and Gunshot Detection in Noisy Environments,” Proceedings of the 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, 3-7 September 2007, pp. 1216-1220.
[3]	C. Zieger, A. Bruti and P. Svaizer, “Acoustic Based Surveillance System for Intrusion Detection,” Proceedings of the 6th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2009), Genoa, 2-4 September 2009, pp. 314-319. doi:10.1109/AVSS.2009.49
[4]	A. Dufaux, “Detection and Recognition of Impulsive Sounds Signals,” Institute de Microtechnique Neuchatel, Switzerland, 2001.
[5]	M. Q. Wu, W. Ma and C. X. Xu, “A Low-Power Algorithm of Uniting the Time-Domain and Frequency-Domain Thresholds,” China Patent, No. 101848044, 2010.
[6]	H. Urknowitz, “Energy Detection of Unknown Deterministic Signals,” Proceedings of the IEEE, Vol. 55, No. 4, 1967, pp. 523-531. doi:10.1109/PROC.1967.5573
[7]	L. Vergara, J. Moragues, J. Gosalbez, et al., “Detection of Signals of Unknown Duration by Multiple Energy Detectors,” Signal Processing, Vol. 90, No. 2, 2010, pp. 719-726. doi:10.1016/j.sigpro.2009.08.007
[8]	S. M. Kay, “Fundamentals of Statistical Signal Processing: Detection Theory,” 1st Edition, New Jersey: Prentice-Hall, 1998.
[9]	Y. Li, “A Quick Classification for Area Environmental Audio Data Based on Local Search Tree,” Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology (ESIAT 2009), Wuhan, China, 4-5 July 2009, pp. 569-574. doi:10.1109/ESIAT.2009.15
[10]	V. David and A. Sánchez, “Advanced Support Vector Machines and Kernel Methods,” Neurocomputing, Vol. 55, No. 1-2, 2003, pp. 5-20. doi:10.1016/S0925-2312(03)00373-4.