Abstract

'''The key to an acoustic signal lies in its frequency spectrum characteristics, and the primary premise of noise reduction is the identification of main noise sources. In this paper, an approach to noise source identification is introduced based on the improved fast independent component analysis (FastCA) algorithm for blind source signals to correct the uncertainty of traditional FastICA. Taking the measured noise signals radiated from a forklift at idle speed as an application case, two obvious estimated independent components (EICs) and their corresponding frequency spectrums were obtained. In addition, the result of numerical identification of sound sources is verified by scaning and paint system (SPS) and the error is less than 5%. Base on the spectral characteristics analysis, the improvement measurement of the target forklift was performed, and the results indicated that the sound power level of radiated noise from the whole vehicle is effectively reduced by 1.75dB, and meets the domestic industry requirement.