Table 4 Significant works on infant cry detection
Literature | Dataset | Features | Classifiers | Performance |
|---|---|---|---|---|
Chang [48] (2019) | Self-recorded (crying with TV, speech, etc.) | Spectrogram | CNN | 99.83% |
Manikanta [25] (2019) | Recorded in homes (crying with ac, fan, etc.) | MFCC | 1D-CNN, FFNN, SVM | 86% |
Dewi [64] (2019) | Self-recorded (cry and not cry) | LFCC | KNN | 90% |
Gu [16] (2018) | Self-recorded (crying with laughter, barking, etc.) | LPC | Dynamic time warping | 97.1% |
algorithm | ||||
Ferretti [18] (2018) | Real Dataset: recorded in the NICU of a hospital; | Log-Mel Coefficients | CNN | 86.58% on real dataset, |
Synthetic DB: crying with speech, “beep” sounds, etc.) | 92.92% on synthetic | |||
dataset | ||||
Feier [87] (2017) | TUT Rare Sound Events 2017 (crying with | Log-amplitude mel-spectrogram | CRNN | 85% for baby crying detection, |
“glass breaking”, “gunshot”, etc.) | 87% for all | |||
three targets | ||||
Torres [27] (2017) | Online resources (crying with adult | Voiced unvoiced counter, Consecutive F0 | Support Vector Data | AUC 92% |
cry, vacuum cleaning, etc.) | and harmonic ratio accumulation,MFCC | Description (SVDD),CNN | ||
Lavner [17] (2016) | Recorded in domestic environment (crying | MFCC, Pitch, Formants, etc. | CNN | 95% |
with speech, door opening, etc.) |