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Biometrics from heart sounds: Evaluation of a new approach based on wavelet packet cepstral features using HSCT-11 database

机译：心音的生物特征识别：使用HSCT-11数据库评估基于小波包倒谱特征的新方法

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This paper introduces a new approach for human recognition using heart sounds. The main contribution of this paper involves adopting wavelet packet cepstral coefficients as new features for heart Sound signals in biometric applications. The proposed features utilize a non-linear wavelet packet filter banks which are designed to match the acoustic nature of the heart sound. The proposed system is evaluated using an open database for heart sounds known as HSCT-11 which contains data collected from 206 users. Based on the achieved results, the proposed system can identify users with best accuracy of 91.05% and verify them with an equal error rate of 3.2% using 200-fold random validation (random sub-sampling). The experimental results showed higher correct recognition rates and lower error rates in identification and verification modes, respectively, compared to previously implemented systems evaluated on the same database (HSCT-11). (C) 2016 Elsevier Ltd. All rights reserved.
机译：本文介绍了一种使用心音进行人类识别的新方法。本文的主要贡献涉及采用小波包倒频谱系数作为生物识别应用中心音信号的新功能。提出的特征利用非线性小波包滤波器组，其被设计为匹配心音的声学性质。建议的系统使用称为HSCT-11的开放式心音数据库进行评估，该数据库包含从206位用户收集的数据。基于所获得的结果，所提出的系统可以识别最高准确率为91.05％的用户，并使用200倍的随机验证（随机子采样）以3.2％的相等错误率验证用户。实验结果表明，与先前在同一数据库（HSCT-11）上评估的系统相比，分别在识别和验证模式下的正确识别率更高，错误率更低。 （C）2016 Elsevier Ltd.保留所有权利。

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