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Table 2 Significant works on infant cry reason classification

From: A review of infant cry analysis and classification

First author Dataset Features Classifiers Best performance
Felipe [43] (2019) iCOPE (pain vs. no pain) Mel Scale (MS), MFCC, SVM 71.68%
   Constant-Q Chromagram (CQC)   
   Local Binary Pattern (LBP),   
   Local Phase Quantization (LPQ),   
   Robust Local Binary Pattern (RLBP)   
   extracted from spectrogram   
Sharma [39] (2019) Donate A Cry (hungry, burp needed, Mean frequency; standard deviation; K-means clustering, 81.27%
  belly pain, discomfort, tired, lonely, median frequency; third quartile range; hierarchical clustering, Gaussian  
  feeling cold/hot, is scared, unidentified) spectral entropy; kurtosis, skewness, mixture models clustering  
   spectral flatness, etc.   
Maghfira [36] (2019) Dunstan Baby Database (pain, hunger, Spectrogram CNN-RNN 94.97%
  discomfort, need to burp, belly pain)    
Franti [9] (2018) Dunstan Baby Database (pain, hunger, Spectrogram CNN 89%
  discomfort, need to burp, belly pain)    
Liu [13] (2018) NICU recorded (draw attention cry, LPC, LPCC, MFCC, BFCC Nearest Neighbor 76.4%
  diaper change needed cry, and hungry)   Artificial Neural Network  
Turan [41] (2018) CRIED Spectrogram Capsule Network 86.1%
Osmani [67] (2017) Dunstan Baby Database (hunger, spectrum, pitch, zero-crossing rate, SVM, Bagging Decision Tree, N/A
  pain, tiredness, belly pain, need burp) root mean square, intensity, energy along and Boosted Trees,  
   with their calculated statistics (mean,   
   variance, skewness, etc)   
Chang [50] (2016) Collected from National Taiwan Spectrogram CNN 78.5%
  University Hospital (hungry, pain, and sleep)    
Bano [20] (2015) Self-recorded (hungry, need to Pitch short-time energy MFCC KNN 86%
  burp, sleepy, pain, discomfort) Statistical properties of MFCC   
Orlandi [21] (2015) Self-recorded (full term vs. preterm) CU length, F0 median, F0 mean, Logistic regression, 87%
   F0 standard deviation, F0 minimum, Multilayer perceptron NN,  
   F0 maximum, number of estimated Support Vector Machine,  
   F0 values, F123 median, and Random Forest  
   F123 mean, F123 standard deviation,   
   F123 minimum, F123 maximum   
Bhagatpatil [22] (2015) Self-recorded (pain, hunger, discomfort, LFCC, MFCC K-mean clustering, KNN 91.58%
  need to burp, belly pain)    
Rosales-Pérez [71] (2014) Baby Chillanto (hungry vs. pain) MFCC, LPC Fuzzy model 97.96%
Yamamoto [23] (2013) Self-recorded (discomfort, hungry, sleepy) FFT Nearest neighbor 62.1%