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Active Noise Control
 

[BASIC CONCEPTS | ADAPTIVE ALGORITHMS | APPLICATION ]
1. BASIC CONCEPTS

Conventional methods of suppressing acoustic noise using passive sound absorbers generally do not work well at low frequencies. This is because at these low frequencies the acoustic wavelengths become large compared to the thickness of a typical acoustic absorber. For example, a sound wave of frequency 100 Hz will have a wavelength of about 3.4 meters in air under normal conditions. It is also difficult to stop low frequency sound being transmitted from one space to another unless the intervening barrier is very heavy. For these reasons, a number of practically important acoustic noise problems are dominated by low frequency contributions. These problems are sometimes difficult to solve using passive methods since the solutions are expensive in terms of weight and bulk.

Active noise control exploits the long wavelengths associated with low frequency sound. It works on the principle of destructive interference between the sound fields generated by the original primary sound source(reference) and that due to other secondary sources (control output, commonly loudspeaker), whose acoustic outputs can be controlled.


Figure 1-1. ANC system

Figure 1-2. ANC of DUCT system


There are two distinct strategies for active noise control; feedforward and feedback. For feedforward control 'reference signals' that are assumed to cause noise in the zone of interest are measured and are linearly combined to give appropriate output to the secondary speaker. The result of cancellation fed from the error monitoring microphone is used to optimize the weights of the controller. For both of reference and error signal measure, microphone is commonly used via low pass filter. For control output, loudspeaker is commonly used.

[BASIC CONCEPTS | ADAPTIVE ALGORITHMS | APPLICATION ]
2. ADAPTIVE ALGORITHMS

Controller involves adaptive algorithms. Most of them are algorithms of LMS family and most common one is the Filtered-X LMS algorithm. To enhance its convergence characteristics we have developed Constraint filtered-X LMS algorithm that have better property in convergency than others.


Figure 2-1. Adaptation Structure of Adaptive Notch FIlter



Figure 2-2. Block diagram of ANC with reference generator



Figure 2-3. Block diagram of filtered-X LMS algorithm



Figure 2-4. Block diagram of constraint filtered-X LMS algorithm


[BASIC CONCEPTS | ADAPTIVE ALGORITHMS | APPLICATION ]
3. APPLICATION

A. Road booming noise for a car

Figure 3-1. Road booming system

Figure 3-2. Experimental results

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B. Engine booming noise for a car

      Figure 3-3. Engine booming system


Figure 3-4. Experimental results

C. Cabin noise for a passenger ship

Figure 3-5. ANC system for a ship cabin

Figure 3-6. Experimental Setup

Figure 3-7. Experimental results



[BASIC CONCEPTS | ADAPTIVE ALGORITHMS | APPLICATION ]