What is Active Noise Control ?

Active Noise Control (ANC) is based on the superposition principle by utilizing additional secondary sources to generate “anti-noise” of equal amplitude and opposite (180 degrees out of) phase that cancels the undesired primary noise, resulting in quiet residual noise. In practice, the generation of anti-noise is processed digitally and adaptively to achieve highly precise control, temporal stability, and reliability [1]. The ANC design usually utilizes an appropriate array of microphones as sensors and electronically driven loudspeakers as the secondary sources. The analog components such as loudspeakers, mixers, amplifiers, and microphones used in ANC systems are already available in audio products. This fact allows the integration of both audio and ANC systems into one audio-integrated system sharing analog components. This integrated system developed in 1993 applies audio interference cancellation algorithm [4] to allow both ANC and audio functions work simultaneously without interfere each other, i.e., ANC attenuates only unwanted noise without canceling the desired audio.

In general, there are two basic types of audio-integrated ANC algorithms: feedforward and feedback [1]. The feedforward ANC utilizes a reference microphone at the upstream to provide reference signal of the approaching noise. Primary noise that correlates with the reference signal will be canceled downstream at the location of error microphone. In a feedback ANC system, the reference signal is internally generated as a predicted signal based on the measured error signal. Therefore, the feedback ANC system can only reduce predictable noise, while the feedforward ANC system is capable of reducing broadband noise. In ANC applications such as headphones, the secondary loudspeaker of the ANC system is also used to play intended audio signals (such as speech and music) during the ANC operation. To prevent the ANC system from canceling the desired audio signal and to avoid the audio signal acting as interference that degrades the ANC performance, the audio-integrated ANC algorithm was proposed for solving these problems.

A. Feedforward Audio-Integrated ANC Systems

The feedforward ANC system is exemplified by canceling noise in a duct as illustrated in Fig. 1. The primary noise is produced by the noise source, and a reference microphone is placed upstream for sensing the primary noise before it reaches the downstream secondary loudspeaker in the duct. The reference signal , which is obtained through a preamplifier, an anti-aliasing filter, and an analog-to-digital converter (ADC), is processed by the adaptive filter to generate the anti-noise signal that drives a secondary loudspeaker, through a digital-to-analog converter (DAC), a reconstruction filter, and a power amplifier.

At the downstream, the error microphone senses the residual noise , which is obtained through a preamplifier, an anti-aliasing filter, and an ADC. The adaptive filter minimizes the error signal by adapting filter coefficients automatically using the filtered-X least-mean-square (FXLMS) algorithm [2]. The secondary-path transfer function is from to , which includes the DAC, reconstruction filter, power amplifier, loudspeaker, acoustic path from the loudspeaker to the error microphone, preamplifier, anti-aliasing filter, and ADC. The secondary-path model , which can be estimated either offline [1, 3] or online [4] method, is required by the FXLMS algorithm to compensate for the effects of secondary path.

Fig. 1. Block diagram of the feedforward ANC system.

Based on the feedforward ANC system, the feedforward audio-integrated ANC system is shown in Fig. 2, where the audio signal is mixed with the adaptive filter output , and the composite signal is used to drive the secondary loudspeaker. Thus, the signal picked up by the error microphone contains both the residual noise and the audio component. The audio source is filtered by the secondary-path estimation filter to estimate the audio component picked up by the error microphone, and then the estimated audio component is subtracted from to obtain the audio-free error signal , which is used to update the adaptive filter . Therefore, the adaptive filter performs the adaptive cancelling of the audio component picked up by the error microphone using the least-mean square (LMS) algorithm [5].

As shown in Fig. 2, the adaptive filter performs the adaptive system identification [6] of the secondary path using the audio signal as the excitation signal. In theory, when the audio signal is rich in frequency content and uncorrelated with the anti-noise , a perfect model can be obtained, i.e., . Thus, the error signal used for the FXLMS algorithm is the true residual noise without audio component. Therefore, the performance of the FXLMS algorithm will not be degraded by the additional audio component picked up by the error microphone, and the ANC system will not cancel the desired audio component because the audio component is not fed back to the FXLMS algorithm. The additional benefit of using the audio-integrated algorithm is that the adaptive filter performs the on-line modeling of the secondary path using the audio signal as an excitation signal. Furthermore, because the audio-integrated ANC system uses the same loudspeakers and amplifiers to play the intended audio signal, it adds value to the integrated system without increasing the overall system cost.

Fig. 2. Block diagram of the audio-integrated feedforward ANC system.

B. Feedback Audio-Integrated ANC Systems

The concept of single-channel feedback ANC system is illustrated in Fig. 3 for the duct noise problem. The FXLMS algorithm, secondary-path and its estimation , are the same as the feedforward ANC system. There is no reference microphone, so that the reference signal is synthesized as , where the is the adaptive filter output filtered by the secondary-path model . As shown in Fig. 3, the predicted noise will be equal to the primary noise if . Because the current estimate signal is used as the reference signal for the next iteration, i.e., at time , the reference signal synthesis process is functioned as a one-step predictor. This principle indicates that the adaptive feedback ANC can cancel only predictable noise. Figure 4 shows the audio-integrated feedback ANC system. Its principle is similar to the feedforward ANC structure described in Fig. 2.

Fig. 3. Block diagram of the feedback ANC system.

Fig. 4. Block diagram of the audio-integrated feedback ANC system.

References:

[1] S. M. Kuo and D. R. Morgan, Active Noise Control Systems: Algorithms and DSP Implementations, New York: Wiley, 1996.

[2] D. R. Morgan, “An analysis of multiple correlation cancellation loops with a filter in the auxiliary path,” IEEE Trans. Acoust. Speech Signal Process., vol. 28, no.

4, pp. 454–467, Apr. 1980.

[3] J. C. Burgess, “Chirp design for acoustical system identification,” J. Acoust. Soc. Amer., vol. 91, pp. 1525–1530, Mar. 1992.

[4] L. J. Eriksson and M. C. Allie, “Use of random noise for on-line transducer modeling in an adaptive active attenuation system,” J. Acoust. Soc. Amer., vol. 85, pp. 797–802, Feb. 1989.

[5] B. Widrow and S. D. Stearns, Adaptive Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 1985.

[6] S. M. Kuo et al., “Integrated automotive signal processing and audio system,” IEEE Trans. Consum. Electron., vol. 39, no. 3, pp. 522–532, Aug. 1993.