Active noise control (ANC) is achieved by introducing a cancelling "antinoise" wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples.

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Active noise control: a tutorial review

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 and that due to other secondary sources, acoustic outputs of which can be controlled. The acoustic objectives of different active noise control systems and the electrical control methodologies that are used to achieve these objectives are examined. The importance of having a clear understanding of the principles behind both the acoustics and the electrical control in order to appreciate the advantages and limitations of active noise control is emphasized. A brief discussion of the physical basis of active sound control that concentrates on three-dimensional sound fields is presented. >

Active noise control

This paper presents a Volterra filtered-X least mean square (LMS) algorithm for feedforward active noise control. The research has demonstrated that linear active noise control (ANC) systems can be successfully applied to reduce the broadband noise and narrowband noise, specifically, such linear ANC systems are very efficient in reduction of low-frequency noise. However, in some situations, the noise that comes from a dynamic system may he a nonlinear and deterministic noise process rather than a stochastic, white, or tonal noise process, and the primary noise at the canceling point may exhibit nonlinear distortion. Furthermore, the secondary path estimate in the ANC system, which denotes the transfer function between the secondary source (secondary speaker) and the error microphone, may have nonminimum phase, and hence, the causality constraint is violated. If such situations exist, the linear ANC system will suffer performance degradation. An implementation of a Volterra filtered-X LMS (VFXLMS) algorithm based on a multichannel structure is described for feedforward active noise control. Numerical simulation results show that the developed algorithm achieves performance improvement over the standard filtered-X LMS algorithm for the following two situations: (1) the reference noise is a nonlinear noise process, and at the same time, the secondary path estimate is of nonminimum phase; (2) the primary path exhibits the nonlinear behavior. In addition, the developed VFXLMS algorithm can also be employed as an alternative in the case where the standard filtered-X LMS algorithm does not perform well.

Adaptive Volterra filters for active control of nonlinear noise processes

Active sound attenuation systems may be described using a system identification framework in which an adaptive filter is used to model the performance of an unknown acoustical plant. An error signal may be obtained from a location following an acoustical summing junction where the undesired noise is combined with the output of a secondary sound source. In order for the model output to properly converge to a value that will minimize the error signal, it is frequently necessary to determine the transfer function of the secondary sound source and the path to the error signal measurement. Since these transfer functions are continuously changing in a real system, it is desirable to perform continuous on‐line modeling of the output transducer and error path. In this paper, the use of an auxiliary random noise generator for this modeling is described. Based on a Galois sequence, this technique is easy to implement, provides continuous on‐line modeling, and has minimal effect on the final value of the error signal.

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Use of random noise for on‐line transducer modeling in an adaptive active attenuation system

The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

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Recent Advances on Active Noise Control: Open Issues and Innovative Applications

The use of infinite impulse response (IIR) adaptive filters has lagged behind that of finite impulse response (FIR) adaptive filters. This has been due, in part, to the increased complexity of IIR filters and the potential for instability that exists due to the presence of poles in the transfer function. This paper discusses the use of adaptive filters for the active cancellation of acoustic noise. It is shown that IIR filters possess certain characteristics that are highly desirable for this problem. The selection of an appropriate IIR adaptive algorithm is discussed using observability considerations. It is shown that the recursive least mean square (RLMS) algorithm of Feintuch possesses significant advantages for use in a practical active attentuation system. Results are presented from computer simulations as well as an actual system using a TI TMS32010 digital signal processing microprocessor.

The selection and application of an IIR adaptive filter for use in active sound attenuation

Active noise control utilizes microphones, electronics, and loudspeakers to generate an acoustical wave to cancel undesired sound. The basic approaches to active noise control are briefly reviewed. Primary emphasis is on a system identification approach to the problem using adaptive digital signal processing. A solution to problems caused by the acoustical nature of the application is presented. This includes a discussion of the use of an IIR (infinite-impulse response) adaptive filter to model the primary plant and compensate for acoustic feedback, the use of an independent random noise generator to continuously model the characteristics of the source and error plant, and the effect of uncorrelated noise on both processes. >

Active noise control using adaptive digital signal processing

Active noise control is a real-time application of adaptive digital filtering which requires the full capabilities of a modern digital signal processing module. Even though frequencies are low, so that sample rates of a few kilohertz can be used, the extensive computations involved require efficient, high-speed processing. System hardware was designed to allow software-controlled versatility as well as fully automatic operation of a complete active noise control system. The ability to self-calibrate and self-model are important system features. Adaptive filter lengths determine the algorithm speed and necessary memory requirements. System limitations were primarily caused by on-chip memory size. The use of the Texas Instruments TMS32010 for this application and the improvements possible with the TMS32020 are described. >

R.A. Greiner

Papers

The selection and application of an IIR adaptive filter for use in active sound attenuation

Active noise control using adaptive digital signal processing

Hardware and software considerations for active noise control