A Study of the Virtual Microphone Algorithm for ANC System Working in Audio Interference Environment

Abstract

Two kinds of anti-audio interference active noise control (ANC) algorithms are proposed based on a conventional filtered-X least mean squares virtual microphone (FXLMS-VM) algorithm. The proposed filtered-X least mean squares virtual microphone and anti-audio interference (FXLMS-VM-AI) algorithm employs an extra audio path to compensate for the influence of the audio interference phenomenon on the ANC adaptive filter. The proposed time frequency mixed filtered-X least mean squares virtual microphone and antiaudio interference (TFM-FXLMS-VM-AI) algorithm can overcomes this audio interference effect by calculating the coefficients of the ANC adaptive filter in the frequency domain. The time domain acoustic wave field simulation method is adopted to test the performance of the proposed ANC algorithms in an audio interference environment. By analyzing the simulation results, both proposed algorithms show better convergence accuracy in terms of the adaptive filter coefficients and better audio signal performance than the conventional algorithm. The proposed FXLMS-VM-AI algorithm has a faster convergence speed for the ANC adaptive filter than the proposed TFM-FXLMS-VM-AI algorithm. The proposed TFM-FXLMS-VM-AI algorithm has a lower computational complexity than the conventional algorithm and the proposed TFM-FXLMS-VM-AI algorithm.

Active structural-acoustic control of interior noise in a vibro-acoustic cavity incorporating system identification

“…Xu el al. 17 proposed a modified FxLMS algorithm for active noise control at a virtual location if an audio interference environment is present. The objective was to cancel out undesired noise and meanwhile prevent the audio signal from being distorted by the ANC system.…”

An adaptive algorithm, based on modified tanh non-linearity and fractional processing, for impulsive active noise control systems

“…The advancements in digital signal processing (DSP) algorithms as well as hardware implementation, over the past few decades, have resulted in successful noise cancellation systems for many practical applications, viz., air conditioning ducts, cars, aircrafts, headphones, and so on. 9–14…”

“…The advancements in digital signal processing (DSP) algorithms as well as hardware implementation, over the past few decades, have resulted in successful noise cancellation systems for many practical applications, viz., air conditioning ducts, cars, aircrafts, headphones, and so on. [9][10][11][12][13][14] The most successful scenario for active control of acoustic noise is a single-channel situation, which comprises one primary (reference) microphone, one secondary (cancelling) loudspeaker, and one error microphone. The reference microphone picks-up the reference noise in advance.…”

Active noise control using wavelet function and network approach

“…In comparison to other ANC approaches, it is easy to implement and has less complexity. The ease of implementation has led to its use in various applications with neural networks [21][22][23][24] such as transportation noise control, appliances noise and industrial noises. ANC systems can be classified into two categories: feedforward control and feedback control.…”