 Open Access
 Total Downloads : 449
 Authors : G. Suresh
 Paper ID : IJERTV1IS6217
 Volume & Issue : Volume 01, Issue 06 (August 2012)
 Published (First Online): 11102012
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Normalized Adaptive Method for Hearing Protection in Industries
Normalized Adaptive Method for Hearing Protection in Industries
G.Suresh
Principal, Gnanavikasa Polytechnic, Challakere road, Chitradurga577501, Karnataka.
sureshnov27@yahoo.co.in
ABSTRACT: 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. Several investigations on industrial noise proved that industrial workers need at least 1015 dB higher SNR (Signal to Noise Ratio) than the other places. The objective of this paper is to implement Normalized Least Means Square (NLMS) algorithm to reduce the effect of industrial noise and to improve overall sound quality of industrial workers. The computer simulations results show superior characteristics of the algorithm by improving the SNR of more than 15dB and noise reduction performance is better than single source adaptive algorithm. 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.
Keywords: Industrial noise, Hearing protection, Hearing loss, adaptive filter, SNR improvement, NLMS.

Introduction
Highintensity noises are a health hazard for industrial workers, and hearing protection is necessary to prevent hearing loss. Hearing loss caused by occupational noise is one of our biggest industrial diseases. It is a disease that has been recognized since the Industrial Revolution. The conventional passive methods, such as ear muffs, are ineffective against lowfrequency noise [3]. This problem can be effectively solved by using the adaptive algorithms for different frequencies [4]. The main intension of this paper is to highlight the filtering nature of NLMS technique to reduce the effects of industrial noise on workers and to improve overall sound quality for industrial workers.
Many researchers has stated that [7] noise can not only cause hearing impairment due to longterm exposures of over 85 dB, but it also acts as a causal factor for stress and raises systolic blood pressure. Additionally, it can be a causal factor in work accidents, both by masking hazards and warning signals, and by impeding concentration [12]. Noise also acts synergistically with other hazards to increase the risk of harm to workers [2]. [10] States that exposure to 85 dB of noise for more than eight hours per day can result in permanent hearing loss. Since decibels are based on a logarithmic scale, every 3 dB sound pressure level increase results in a doubling of intensity, meaning hearing loss can occur at a faster rate. Therefore, gradual developing industrial noise induced hearing loss occurs from the combination of sound intensity and duration of exposure.
Noise induced hearing problems are typically is centered at 4000 Hz. The louder the noise is, the shorter the safe amount of exposure is. Normally, the safe amount of exposure is reduced by a factor 2 for every additional 3 dB. For example, the safe daily exposure
amount at 85 dB is 8 hours, while the safe exposure at 91 dB is only 2 hours [8], [9]. Sometimes, a factor 2 per 5 dB is used. Personal electronic audio devices, such as iPods, because iPods often reaching 115 decibels or higher. This can produce powerful enough sound to cause significant hearing loss in the workers, given that lesser intensities of even 70 dB can also cause hearing loss [11]. Different kinds of filtering methods are suggested in the literature for the minimization of noise in industries [5], [6]. However, through the proper use of ear protection, education, hearing conservation programs in the workplace, and audiological evaluations, industrial noise induced problems can be reduced [13]. Hence in this paper, suggests the NLMS noise cancellation algorithm for hearing protection of the industry workers.

NLMS Noise Canceller
Adaptive filters can adapt their filter coefficients to the environment according to preset rules. Adaptive system with directional microphones is the best way to improve SNR in industries to protect workers [1]. Hence, in this work the NLMS is implemented to reduce the effect of noise in industries. In the adaptive noise cancellation process, the
desired speech signal
d (n)
is to be calculated from a noisecorrupted speech
signal x(n)
d (n)
v1 (n) . In this method, reference signal, v2 (n)
is correlated with v1 (n) .
This reference signal may be used to estimate the noise v1 (n) , and the estimate can be then
be subtracted from x(n) to get the estimate of d (n) .
d(n)
x(n)
v1 (n) 1
If d (n) , and
v1 (n)
are jointly widesense stationary processes, and if the autocorrelation
v
r (k )
2
and the crosscorrelation
r (k)
v v
1 2
are known, then filter can be designed to find the
minimum means square estimate of v1 (n) . If the reference signal
v2 (n)
is uncorrelated
with d (n) , then the minimization of mean square error E{ e(n)2 }is equivalent to
minimizing
E{ v (n) v (n) 2 } . 2
1 1
Figure 1 Adaptive noise canceller
The output of the adaptive filter is the minimum mean square estimate of the noise v1 (n) . Since the output of the adaptive filter is the minimum mean square estimate of v1 (n) , then it
follows that e(n) is the minimum mean square estimate of the desired speech signal d (n) . It
is an efficient noise canceller and is implemented in ear cap (or any other similar device) for reducing noise effects.

Performance evaluation
Performance of the adaptive filters are measured, compared and analyzed with the help of following parameters.

Convergence rate: The convergence rate determines the rate at which the filter converges to its resultant state. Usually faster convergence rate is the desired characteristic of an adaptive system. Convergence rate is not, however, independent of all other performance characteristics. If the convergence rate is increased, the stability characteristics will decrease, making the system more likely to diverge instead of converge to the proper solution. In this work, convergence rate is measured in terms of eigenvalue ratio.

Minimum mean square error (MSE): The MSE is a metric indicating how well a system can adapt to a given solution. A small minimum MSE is an indication that the adaptive system has accurately modeled, predicted, adapted and/or converged to a solution for the system.

Stability: Stability is probably the most important performance measure for the adaptive system. The algorithm convergence time and stability depends upon the ratio of the largest to the smallest eigenvalue associated with the correlation matrix of the input sequence. Therefore, stability of the algorithm is defined in terms of eigenvalue ratio.

Eigenvalue ratio: Eigenvalue ratio or the eigenvalue spread is the ratio between the maximum eigenvalue and the minimum eigenvalue of the input autocorrelation matrix. The eigenvalue ratio r can be calculated as
r max 3
min
Where max and min are the maximum and minimum eigenvalues, which found on the main diagonal of the autocorrelation matrix. Then the rate of convergence can be calculated as
C.rate
(r 1)2
(r 1)2 4
From the above equation it is clear that, the convergence time decreases if the eigenvalue ratio increases and vice versa.

SNR: Amount of noise filtering can be measured from adaptive system with the help of input SNR and output SNR. Input SNR is the ratio between the power of input signal and power of noise at input. Output SNR is the ratio between the power of filtered signal and power of noise at output. In general SNR is defined as

SNR
x2 (n)
n
e2 (n)
n
and
SNR(dB) 10 log10
x2 (n)
n 5
e2 (n)
n
Where,
x(n)
is the input signal and e(n)
is the noise.
The algorithm is evaluated for different types of industrial noises with different SNR. In
this work
x(n)
is the speech signal and
e(n)
is the industrial noise. Results show that, both
parameters SNR and eigenvalue ratio are strongly depending on type of noise.
Input SNR in dB 
Output SNR in dB 
Eigenvalue ratio 
0 
19.1 
1005 
5 
23.3 
1012 
10 
25.2 
1001 
Table.1 Performance of NLMS Noise canceller
For different input SNR, the output SNR and eigenvalue ratios are calculated as shown in Table 1. The eigenvalue ratio is calculated to find out how well the algorithm converges to the optimum Wiener solution. The main disadvantage of this algorithm is its high eigenvalue ratio. Because, filters having high eigenvalue ratio requires longer time to converge and vice versa.
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. NLMS method can be used for noise reduction in industries to protect workers. Offline tests in different conditions show improvements in SNR up to 20 dB for zero dB input SNR and the overall performance is better than single source adaptive algorithm. In a more realistic environment, they may have the variations of Â± 5 dB. But, the results show that the algorithms have high eigenvalue ratio. Hence, they need more time to converge into the optimal solution.

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