Recognition of infant cries using wavelet derived mel frequency feature with SVM classification

Tejaswini, S; Sriraam, N.; Pradeep, G C M

doi:10.1109/cimca.2016.8053313

Cited by 9 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also performs well in infant cry reason classification. The Discrete Wavelet Transform MFCC (DWT-MFCC) features work well with SVM and neural network architectures [31,33,51,73].…”

Section: Other Relevant Domain Featuresmentioning

confidence: 99%

A review of infant cry analysis and classification

Mudiyanselage

Gao

et al. 2021

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

This paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

show abstract

“…It also performs well in infant cry reason classification. The Discrete Wavelet Transform MFCC (DWT-MFCC) features work well with SVM and neural network architectures [31,33,51,73].…”

Section: Other Relevant Domain Featuresmentioning

confidence: 99%

A review of infant cry analysis and classification

Mudiyanselage

Gao

et al. 2021

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

show abstract

“…Infants' actively regulate acoustic information in their vocalizations to express specific needs. For example, acoustical analysis of cries has been used to identify the stimulus to cry, whether hunger, pain, or discomfort [29]. Similarly, babies vocalize differently according to their health status.…”

Section: Infant Crymentioning

confidence: 99%

Assessing Mothers’ Postpartum Depression From Their Infants’ Cry Vocalizations

Gabrieli

Bornstein

Manian

et al. 2020

Behavioral Sciences

View full text Add to dashboard Cite

Postpartum Depression (PPD), a condition that affects up to 15% of mothers in high-income countries, reduces attention to the needs of the child and is among the first causes of infanticide. PPD is usually identified using self-report measures and therefore it is possible that mothers are unwilling to report PPD because of a social desirability bias. Previous studies have highlighted the presence of significant differences in the acoustical properties of the vocalizations of infants of depressed and healthy mothers, suggesting that the mothers’ behavior can induce changes in infants’ vocalizations. In this study, cry episodes of infants (N = 56, 157.4 days ± 8.5, 62% firstborn) of depressed (N = 29) and non-depressed (N = 27) mothers (mean age = 31.1 years ± 3.9) are analyzed to investigate the possibility that a cloud-based machine learning model can identify PPD in mothers from the acoustical properties of their infants’ vocalizations. Acoustic features (fundamental frequency, first four formants, and intensity) are first extracted from recordings of crying infants, then cloud-based artificial intelligence models are employed to identify maternal depression versus non-depression from estimated features. The trained model shows that commonly adopted acoustical features can be successfully used to identify postpartum depressed mothers with high accuracy (89.5%).

show abstract

“…Infants' actively regulate acoustic information in their vocalizations to express specific needs. For example, acoustical analysis of cries has been used to identify the reason that induced a baby to cry, whether hunger, pain, or discomfort [22]. Similarly, babies vocalize differently according to their health status.…”

Section: Infant Crymentioning

confidence: 99%

Assessing Mothers' Post-Partum Depression from Their Infants' Cry Vocalizations

Gabrieli¹,

Bornstein²,

Manian³

et al. 2019

Preprint

View full text Add to dashboard Cite

Postpartum depression (PPD), a condition that affects up to the 15% of mothers in high-income countries, reduces attention toward the needs of the child and it is among the first causes of infanticide. PPD is usually identified using self-report measures and therefore the diagnosis may not always be valid. Previous studies highlighted the presence of significant differences in the acoustical properties of the vocalizations of children of depressed and healthy mothers. In this study, cry episodes of infants of depressed and non-depressed mothers are analyzed to investigate the possibility that a machine learning model can identify PPD in mothers from the acoustical properties of infants' vocalizations. Acoustic features (F0, F1-4, Intensity) are first extracted from recordings of crying infants, then novel cloud-based artificial intelligence models are employed to identify maternal depression versus non depression from estimated features. Trained model shows that commonly adopted acoustical features can be successfully used to individuate Post-Partum Depressed mothers with very high accuracy (89.5%).

show abstract

Recognition of infant cries using wavelet derived mel frequency feature with SVM classification

Cited by 9 publications

References 10 publications

A review of infant cry analysis and classification

A review of infant cry analysis and classification

Assessing Mothers’ Postpartum Depression From Their Infants’ Cry Vocalizations

Assessing Mothers' Post-Partum Depression from Their Infants' Cry Vocalizations

Contact Info

Product

Resources

About