摘要
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%.
出处
《弹箭与制导学报》
CSCD
北大核心
2010年第2期240-242,共3页
Journal of Projectiles,Rockets,Missiles and Guidance
关键词
特征提取
小波包分析
鲁棒性
识别率
feature extraction wavelet packet analysis
robust
recognition rate