Spectrogram Image Analysis of Error Signals for Minimizing Impulse Noise

Abstract

This paper presents the theoretical and experimental study on the spectrogram image analysis of error signals for minimizing the impulse input noises in the active suppression of noise. Impulse inputs of some specific wave patterns as primary noises to a one-dimensional duct with the length of 1800 mm are shown. The convergence speed of the adaptive feedforward algorithm based on the least mean square approach was controlled by a normalized step size which was incorporated into the algorithm. The variations of the step size govern the stability as well as the convergence speed. Because of this reason, a normalized step size is introduced as a new method for the control of impulse noise. The spectrogram images which indicate the degree of the attenuation of the impulse input noises are considered to represent the attenuation with the new method. The algorithm is extensively investigated in both simulation and real-time control experiment. It is demonstrated that the suggested algorithm worked with a nice stability and performance against impulse noises. The results in this study can be used for practical active noise control systems.

Characteristics of Relocated Quiet Zones Using Virtual Microphone Algorithm in an Active Headrest System

“…With the advancement of the embedded processors, active noise control (ANC) to cancel an unwanted noise with antinoise by taking the principle of superposition has been studied in many industrial applications [1][2][3][4][5]. In particular, in the case of a vehicle engine, the adaptive feedforward algorithms such as the FxLMS (filtered-reference least mean square) have been applied successfully to actively control a narrowband interior noise in a car cabin generated by the engine [2,3,5].…”