Improving Adaptive Filters for Active Noise Control Using Particle Swarm Optimization

Improving Adaptive Filters for Active Noise Control Using Particle Swarm Optimization

Rodrigo P. Monteiro (University of Pernambuco, Recife, Brazil), Gabriel A. Lima (University of Pernambuco, Recife, Brazil), José P. G. Oliveira (FITec, Recife, Brazil), Daniel S. C. Cunha (FITec, Recife, Brazil) and Carmelo J. A. Bastos-Filho (University of Pernambuco, Recife, Brazil)
Copyright: © 2018 |Pages: 18
DOI: 10.4018/IJSIR.2018100103

Abstract

The excessive exposure to certain kinds of acoustic noise can lead to health problems. To avoid this situation, the use of noise attenuation devices is a standard solution. Among those devices, the active noise control (ANC) systems have gained prominence over the years, mainly due to the technological development and costs reduction of electronic components. Despite good performance of ANC concerning low-frequency noise attenuation, the convergence speed for this kind of system is still an important issue when it deals with real-time applications in dynamic environments. This article presents an alternative solution to accelerate the active attenuation system response. This solution is based on the use of sets of coefficients, which are employed during the adaptive filter initialization and are obtained via a training process with particle swarm optimization (PSO). Two objective functions were tested: one based on the response time itself and the other one based on the magnitude reduction of the residual noise. The coefficients obtained through this process provided response time reductions up to 98.3% concerning adaptive filters initialized with null coefficients. The article is an extended version of the conference paper Accelerating the Convergence of Adaptive Filters for Active Noise Control Using Particle Swarm Optimization, published in LA-CCI 2017.
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1. Introduction

Acoustic noise is traditionally defined as any unwanted or disturbing sound (EPA, 2017). The excessive exposure to certain noises is a severe problem in large cities and can lead to health problems such as hearing loss and stress-related illnesses (FIEP, 2016). Industrial plants, commercial establishments, and vehicles are some examples of noise generation sources (Harris, 1991).

The approaches employed in the control of the acoustic noise are divided into passive and active methods (Kuo, et al., 1996). The passive techniques regard the use of enclosures, barriers, and silencers to reduce the undesired noise. They present reasonable attenuation properties for a broad range of frequencies (Kuo, et al., 1996; Kuo & Morgan, 1999). Although this sort of solution is widely implemented in vehicles and large industrial equipment, it is relatively expensive, physically large and does not work well at the mitigation of low-frequency sounds (Elliott & Nelson, 1999; Kuo, et al., 1996; Kuo & Morgan, 1999; Xu, 2010).

On the other hand, the active approaches, commonly referred as Active Noise Control (ANC), require less space (Kuo & Morgan, 1999) and present a good performance at low-frequency sounds attenuation (Elliott & Nelson, 1999; Kuo & Morgan, 1999; Xu, 2010). Furthermore, the technological development allowed the construction of more powerful and economical electronic devices, which favored the active noise attenuation systems (Xu, 2010). This type of approach works according to the physical principle of waves destructive interference. The ANC gathers information about the environmental noise (primary noise) and then generates a second waveform (secondary noise) of equal amplitude and opposite phase concerning the former one. Both waveforms cancel each other in a region of the space, creating a silent zone (Elliott & Nelson, 1999; Kuo, et al., 1996; Kuo & Morgan, 1999; Xu, 2010).

One of the most remarkable characteristics of the active systems is their capability of adapting themselves to the environmental noise (Kuo, et al., 1996; Kuo & Morgan, 1999; Xu, 2010). However, this is not an immediate process, i.e., the adaptive filter requires a processing time to adjust itself to the reference signal, which is a critical issue when the system deals with dynamic environments on real-time applications. This transient behavior can be observed in Figure 1, that shows how the resulting noise changes over time during the attenuation of a stationary acoustic signal. A Finite Impulse Response (Paulo, 2008) feedforward system based on a Least Mean Squares (LMS) (Paulo, 2008) algorithm was employed in this example.

Figure 1.

Amplitude of the resulting noise along the time for a regular feedforward LMS system

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