Open Access Open Access  Restricted Access Subscription or Fee Access

Ambulance Siren Noise Reduction Using Active NOISE Control System with FXLMS

Manisha Sheoran, Dr. Suresh Kumar, Deepak Sharma


Acoustic noise problems have become major issue in present technical era due to increased growth of machines, transport equipments and industrial apparatus, high speed wind buffeting and many other noise sources.  Active noise control (ANC) is a particular cancelling system, based on superimposition principle, that is, a conflicting noise with equal amplitude but converse phase is produced by secondary source to nullifying the undesirable noise acoustically. In last few decades, several adaptive algorithms are developed for nullification of noise. Here we use some specific algorithm like filtered-x least mean square (FxLMS) algorithm in ANC system in which secondary sources are interlinked via some electronic system. High level noise is dangerous for human life but it is more exasperating for patients specially. In this paper, ANC system based on filtered-x least mean square algorithm is applied to ambulance siren noise. The simulations performed on MATLAB show that noise level is reduced by approximately 36-38 dB, which is a substantial reduction in level of noise. Simulations results show its effectiveness and robustness.


ANC; Secondary Path Modeling; FxLMS; Acoustic Noise; Filters.

Full Text:



Yoel N., “Hearing Protection: Eliminating Noise Pollution in IT Work Environments”, Occupational Health and Safety Magazine, March,2011.

Dobbie R. A., “Noise. Physical and Biological Hazards of the Workplace”, second Edition. Ed: Wald, P.H., Stave, G. New York: John Wiley & Sons, Inc; 2002.

Kuo S. M., Morgan D. R., “Active Noise Control Systems—Algorithms and DSP Implementations”, Wiley, New York, 1996.

Elliott S. J., “Signal Processing for Active Noise Control”, London, Academic, U.K., 2001.

Kuo S. M. and Morgan D. R., “Active noise control: A tutorial review,” Proc. IEEE, vol. 87, no. 6, pp. 953–973, Jun. 1999.

Morgan D. R., “An analysis of multiple correlation cancellation loops with a filter in the auxiliary path,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-28, pp. 454–467, Aug. 1980.

Widrow B., Shur D., and Shaffer S., “On adaptive inverse control,” in Proc. 15th Asilomar Conf., pp. 185–189, 1981.

Burgess J. C., “Active adaptive sound control in a duct: A computer simulation,” J. Acoust. Soc. Amer., vol. 70, pp. 715–726, Sept. 1981.

Widrow B. and Stearns S. D., “Adaptive Signal Processing”. Englewood Cliffs, NJ: Prentice-Hall, 1985.

Sharma M.K. and Vig R., “Server Noise: Health Hazard and its Reduction using Active Noise Control” in Proc. . Int. Conf. IEEE Engineering and computational science (RAECS), 06 – 08 March, 2014.

Boucher, C.C.; Elliott, S.J.;Nelson, P.A.: Effects of modeling errors in the plantmodel on the performance of algorithms for adaptive feedforward control. IEE Proc. F Radar Signal Process., 138 (4) (1991), 313–319.

Wu, M.; Qiu, X.; Chen, G.: The statistical behavior of phase error for deficient-order secondary path modeling. IEEE Signal Process. Lett., 15 (2008), 313–316.

Elliott, S.J.; Nelson, P.A.: Active noise control. IEEE Signal Process.Mag., 10 (4) (1993), 12–35.

Manisha et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(4), April- 2015, pp. 1228-1233

Yoshinobu kajikawa ET AL.APSIPA Transactions on Signal and Information Processing / Volume 1 / December 2012 / e3 DOI: 10.1017/ATSIP.2012.4, Published online: 28 August 2012, 3-13


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.