Noise and vibration problems have become serious with the increased use of industrial equipment, such as machine tools, engines, fans, blowers, transformers, and compressors. It is especially prominent for home care (e.g. pillows and headrests), medical equipment (e.g., hearing aids, magnetic resonance imaging (MRI) systems, and infant incubators), consumer electronics (e.g., helmets, headsets, headphones, air-conditioners, refrigerators, washing machines, kitchen hoods, and vacuum cleaners), transportation (e.g., vehicles, trains, airplanes, and ships), and human activities (e.g., crowded public spaces, offices, and bedrooms). Traditional noise and vibration reduction techniques are based on passive control, such as earplugs, ear-protectors, sound insulation walls, mufflers, sound and vibration-absorbing materials. These passive techniques are effective for reducing noise and vibration over a wide frequency range. However, they require relatively large and costly materials, and are ineffective at low frequencies. Therefore, the active noise and vibration control (ANVC) developed in the early 20th century has gained intensive applications in the last two decades to reduce low-frequency noise. ANVC is based on the principle of superposition, that is, an anti-noise and anti-vibration with the same amplitude but opposite phase are generated by secondary source(s) to cancel unwanted (primary) noise and vibration, resulting in reduced residual noise and vibration. The ANVC system is very efficient for attenuating low-frequency noise and vibration in cases where the passive control techniques are expensive, bulky, and ineffective.

In practical applications, the characteristics of the noise and vibration source and environment are changing, thus the frequency content, amplitude, and phase of the primary noise and vibration are also changing. To deal with these time-varying issues, most ANVC systems utilize adaptive filters to track these variations and unknown plants.

The development of powerful, low-cost digital signal processors (DSPs) encourages the implementation of advanced adaptive algorithms to achieve faster convergence, increased robustness to interference, and improved system performance for practical ANVC applications. The control structure of ANVC is generally classified into two classes: feedforward control and feedback control. In the feedforward control, a reference sensor (e.g., microphone or accelerometer) is used to sense a reference noise or vibration for the adaptive filter to reduce correlating noise or vibration . The feedforward ANVC scheme utilizes a secondary loudspeaker or a shaker (e.g., actuator) to generate anti-noise or anti-vibration, and an error sensor (e.g., microphone or accelerometer) to pick up residual noise or vibration, which serves as the error signal for updating the adaptive filter. The feedback ANVC system uses only an error sensor and a secondary source without using an “upstream” reference sensor.

Recently, many advanced signal processing algorithms, implementation techniques, and successful applications of ANVC have been reported, and several ANVC products are commercially available.