Singularity distribution entropy analysis of impulsive acoustic signals

In order to effectively analyze and calibrate the singularity difference of impulsive acoustic signals in complex environment with low signal-to-noise ratio, a singularity distribution entropy features analysis model based on the mode maximum theory is proposed. Firstly, the impulsive signal is normalized and wavelet transform is carried out to calculate the mode maximum and its specific distribution at each scale, which can reflect the family of mode maximum curves with singular differences. In order to describe the difference quantitatively, entropy is used to describe the distribution of the maximum points which constitute the family of modal maximum curves, and a singular distribution entropy feature model which can effectively analyze the singularity difference of impulsive signals is constructed. The model can describe the singularity difference of signals at low signal-to-noise ratio. Experiments show that the accuracy of 89.25% and 87.63% of typical helicopter impulsive signals (blade-vortex interaction signals and high-speed impulsive signals) can be obtained when the signal to noise ratio is −6dB.