CAJ | 학술논문

目的将倒谱系数提取和高斯混合模型(GMM)相结合,提出了一种基于心音信号的生物识别方法.方法首先心音信号预处理小波去噪,然后进行特征参数的选择,对比研究了线性预测倒谱系数(LPCC)和Mel频率倒谱系数(MFCC),再用高斯混合模型(GMM)进行识别.最后利用50名志愿者的100段心音信号对所提出的方法进行验证.结果对比实验证明LPCC比MFCC更适合用于心音信号的生物识别研究,通过对每段心音信号进行小波去噪,取得了比传统GMM方法更高的识别率.结论表明该方法能够有效提高系统的识别性能,达到了比较理想的识别效果.
목적장도보계수제취화고사혼합모형(GMM)상결합,제출료일충기우심음신호적생물식별방법.방법수선심음신호예처리소파거조,연후진행특정삼수적선택,대비연구료선성예측도보계수(LPCC)화Mel빈솔도보계수(MFCC),재용고사혼합모형(GMM)진행식별.최후이용50명지원자적100단심음신호대소제출적방법진행험증.결과대비실험증명LPCC비MFCC경괄합용우심음신호적생물식별연구,통과대매단심음신호진행소파거조,취득료비전통GMM방법경고적식별솔.결론표명해방법능구유효제고계통적식별성능,체도료비교이상적식별효과.
Objective Extraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal. Methods Firstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment col ects 100 heart sounds from 50 people to test the proposed algorithm. Results The comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM. Conclusion Those results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.