基于小波包分析的战场被动声目标特征提取

The Feature Extraction from Battlefield Passive Acoustic Targets Based on Wavelet Packet Analysis

针对战场环境存在噪声干扰的情况,提出了一种基于小波包分析的声目标特征参数提取方法。该方法将小波包分析和Mel倒谱分析相结合,提高了特征参数的鲁棒性。实验结果表明,在噪声条件下,基于小波包分析的平均识别率比MFCC参数提高6.78%,在信噪比为5dB时,识别率仍能达到94.5%。

Aimed at noise in battlefield, a method was proposed for feature extraction from acoustic targets based on wavelet packet analysis. The feature parameter combined wavelet packet analysis with Mel-frequency cepstrum analysis, and its robustness was improved. The experiment results show that the recognition rate based on wavelet packet analysis was improved by 6.78% compared with MFCC （DMel-frequency eepstrum coefficients） under noisy environment. When the SNR was 5dB. the recognition rate was up to 94.5%.