Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification
Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification
复制标题
DOI：
10.1016/j.cmpb.2017.11.021
复制
发表时间：
2018-03-01
影响因子：
6.1
通讯作者：
Polat, Kemal
中科院分区：
文献类型：
--
作者：
Hariharan, M.;Sindhu, R.;Polat, Kemal
背景和目的：婴儿啼哭信号携带了关于啼哭原因（饥饿、疼痛、困倦和不适）或病理状态（窒息、耳聋、黄疸、早产和自闭症等）的多个层次的信息。因此适合早期诊断。本文提出了一种基于小波包特征和改进的二进制蜻蜓优化特征选择方法相结合的婴儿哭声信号分类方法。第一数据库包含507个正常哭声样本（N）、340个窒息哭声样本（A）、879个聋人哭声样本（D）、350个饥饿哭声样本（H）和192个疼痛哭声样本（P）。第二个数据库包括513例黄疸患儿（J）、531例早产儿（Prem）和45例正常儿童（N）。提取了基于小波包变换的能量和非线性熵（496个特征）、基于线性预测编码（LPC）的倒谱特征（56个特征）、Mel频率倒谱系数（MFCC）（16个特征）。组合特征集由568个特征组成。为了克服维数灾难问题，提出了一种改进的二进制最优化算法（IBDFO）来选择最显著的属性或特征。最后，采用极限学习机（ELM）核分类器对不同类型的婴儿哭声信号进行分类。结果：对婴儿哭声信号进行了两类和多类分类实验。在二元或两类实验中，使用IBDFO选择的特征（568个特征中只有204个）实现了H Vs P的90.18%，A Vs N的100%，D Vs N的100%和J Vs Prem的97.61%的最大准确度。对于多个哭泣信号的分类（多类问题），所选要素可以区分三类（N，A，D）的准确率为100%，七个等级的准确率为97.62%。实验结果表明，所提出的特征提取和选择相结合的方法提供了适当的分类精度，并可用于检测细微的变化，哭泣的信号。(c)2017爱思唯尔B. V.保留所有权利。
Background and objective: Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals.Methods: Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well.Results: Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%.Conclusion: The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. (c) 2017 Elsevier B.V. All rights reserved.