Industrial Noise Reduction by Modified Adaptive Complex Transformation

Industrial noise induced hearing loss is an increasingly prevalent disorder that is the result of exposure to high intensity sounds, especially over a long period of time.Noises of industry can cause partial deafness, interference with communication by speech and annoy. These undesirable effects are best avoided by reducing the noise to acceptable levels. Several investigations on industrial noise proved that industrial workers need at least 10-15 dB higher SNR (Signal to Noise Ratio) than the other places. The objective of this paper is to implement Discrete Fourier Transformation Least Mean Square (DFT-LMS) to reduce the effect of industrial noise and to improve overall sound quality of industrial workers. The computer simulated results show superior convergence characteristics of the adaptive complex transformation algorithm by improving the SNR at least 9dB for input SNR’s less than and equal to 0 dB, with 120 convergence ratio, better time and frequency characteristics. These results suggest that a headset with digital signal processing adaptive algorithm is useful for hearing protection in workplaces with high levels of wide band industrial noise.