Low Cost Real Time Robust Identification of Impulsive Signals

Authors: R. Biondi, G. Dys, G. Ferone, T. Renard, M. Zysman

Abstract:

This paper describes an automated implementable system for impulsive signals detection and recognition. The system uses a Digital Signal Processing device for the detection and identification process. Here the system analyses the signals in real time in order to produce a particular response if needed. The system analyses the signals in real time in order to produce a specific output if needed. Detection is achieved through normalizing the inputs and comparing the read signals to a dynamic threshold and thus avoiding detections linked to loud or fluctuating environing noise. Identification is done through neuronal network algorithms. As a setup our system can receive signals to “learn” certain patterns. Through “learning” the system can recognize signals faster, inducing flexibility to new patterns similar to those known. Sound is captured through a simple jack input, and could be changed for an enhanced recording surface such as a wide-area recorder. Furthermore a communication module can be added to the apparatus to send alerts to another interface if needed.

Keywords: Sound Detection, Impulsive Signal, Background Noise, Neural Network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096626

References:

[1] D. Hoiem, Y. Ke, and R. Sukthankar, "SOLAR: Sound Object Localization and Retrieval in Complex Audio Environments", ICASSP 2005
[2] K.D. Martin: Sound-Source Recognition: a Theory and Computational Method, PhD Thesis, EE and Computer Science Dept., MIT, 1999.
[3] C. Clavel, T. Ehrette, G. Richard: Events Detection for an Audio-Based Surveillance System,, Proc.2005 IEEE Intern. Conf. on Multimedia and Expo, ICME 2005, July 6-9, 2005, Amsterdam, the Netherlands, pp. 1306–1309, 2005.
[4] BR. Stiefelhagen, R. Bowers, J. Fiscus, eds. : Multimodal Technologies for Perception of Humans, International Evaluation Workshops CLEAR 2007 and RT 2007, Berlin, Heidelberg: Springer-Verlag, 2008.
[5] T. Heittola, A. Mesaros, T. Virtanen, A. Eronen : Sound Event Detection in Multisource Environments Using Source Separation, CHiME 2011 Workshop on Machine Listening in Multisource Environments, 2011.
[6] A. Dufaux : Detection and Recognition of Impulsive Sounds Signals, PH.D Thesis, Faculté des Sciences, Université de Neuchatel, 2001.
[7] S. Cavaco, J. Santos Rodeia : Classification of Similar Impact Sounds, Proc. ICISP 2010, Editors: A. Elmoataz et al., Series: LNCS, Number: 6134, Springer- Verlag, pp. 307-314, 2010.
[8] S. Haykin : Neural Networks - A Comprehensive Foundation, Macmillan, 1994.
[9] M.T. Hagan, H.B. Demuth, M. Beale: Neural Network Design, PWS Publishing Company, Boston, USA, 1995.
[10] C. deGroot, D. Wurtz: Plain Back-Propagation and Advanced Optimisation Algorithms: a Comparative Study, Neurocomputing, Vol.6, pp.153-161, 1994.
[11] C.M. Bishop, Neural Networks for Pattern Recognition, Oxford Univ. Press, Oxford, 1995.
[12] S. Lecomte, R. Lengellé, C. Richard, F. Capman, B. Ravera :Abormal Events Detection Using Unsupervised One-Class SVM - Application to Audio Surveillance and Evaluation, 8th IEEE Intern. Conf. on Advanced Video and Signal-Based Surveillance, 2011.
[13] A. Dufaux, L. Besacier, M. Ansorge, F. Pellandini : Automatic Sound Detection and Recognition for Noisy Environment, Proc. EUSIPCO 2000, European Signal Processing Conference 2000, pp. 1033-1036, Tampere, FI, September 5-8, 2000.
[14] T. Zhang and C. Kuo : Hierarchical System for Content-based Audio Classification and Retrieval, Proc. Int. Conf. on Acoustics, Speech, and Signal Processing, 1999.
[15] B. Feiten, S. Gunzel : Automatic Indexing of a Sound Database Using Self-organizing Neural Nets, Computer Music Journal, Vol.18(3), pp.53- 65, 1994