Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound

Dmitry Goldgof; Ghada Zamzmi; Md Sirajus Salekin; Rahul Paul; Rangachar Kasturi; Thao Ho; Yu Sun

REVIEW 3 major objections 5 minor 18 references

Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A lightweight neural network can assess neonatal pain from crying sound as accurately as a network hundreds of times larger.

desk verdict First deep-learning cry pain classifier, but likely detects procedure sounds rather than pain-specific cry features. read the letter →

arxiv 1909.02543 v1 pith:VLG6AXXN submitted 2019-09-05 cs.CV

Md Sirajus Salekin , Ghada Zamzmi , Rahul Paul , Dmitry Goldgof , Rangachar Kasturi , Thao Ho , Yu Sun This is my paper · ORCID

classification cs.CV

keywords neonatalpainassessmentcryingsoundanalysisspectrogramclassificationconvolutionalneuralnetworkN-CNNNICUmonitoringdeeplearninginhealthcare

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a small convolutional neural network can assess whether a neonate is in pain from the spectrogram image of its crying sound, and that this is a viable alternative to current nurse-based scoring. On recordings of 31 neonates collected in a real neonatal intensive care unit (NICU), the proposed N-CNN reaches 96.77% accuracy and an area-under-the-curve (AUC) of 0.94 under leave-one-subject-out cross-validation, matching a much larger VGG16 and beating ResNet50. With a 10-fold protocol, it reaches 91.20% accuracy and 0.91 AUC, above handcrafted acoustic features such as MFCC, LPCC, and fundamental frequency. The motivation is clinical: pain scoring is currently intermittent and observer-dependent, and crying sound remains available when the face or body is occluded.

What carries the argument

The central object is N-CNN, a lightweight three-branch cascaded convolutional network with 72,593 parameters developed for neonatal facial-expression analysis: each branch pools or convolves the input at a different scale, and the branches merge before a final convolutional-pooling layer, letting the network combine coarse structure with edge and blob features. The input representation is the spectrogram, a visual image of an audio signal's frequency content over time, converted to 120×120 images and augmented by frequency shifts and added noise. This turns crying-sound assessment into an image-classification problem over the time–frequency energy distribution.

What would settle it

A decisive experiment would be to take the original procedure-window segments and evaluate the trained model on two altered versions: cry audio with the procedure and background sounds removed, and procedure/background audio with the infant's cry removed. If accuracy collapses in the second version or survives only when procedure sounds are present, the classifier is not assessing the cry.

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Extended reading notes

Core claim

On its own terms, the paper's central discovery is that N-CNN, a three-branch CNN of 72,593 parameters, learns to separate pain from no-pain cry segments directly from 120×120 spectrogram images of audio recorded in NICU noise. The paper reports that it ties a fine-tuned VGG16 at 96.77% accuracy with 0.94 AUC, outperforms a fine-tuned ResNet50 at 83.87% accuracy with 0.83 AUC, and outperforms prior handcrafted methods on the same database. The authors interpret this as proof of feasibility for automated, continuous, sound-based neonatal pain assessment, particularly for infants whose faces or bodies cannot be observed.

Load-bearing premise

The load-bearing premise is that the pain/no-pain labels reflect the infant's cry, because the audio segments were cut to the procedure window and nurses assigned NIPS pain scores before, during, and after the procedure; if the network separates procedure sounds, staff voices, or equipment noise instead, the accuracy does not measure pain assessment.

Editorial extensions

If this is right

Crying sound alone carries enough information to separate pain from no-pain in realistic NICU recordings, so pain monitoring can continue when facial or body cues are hidden.
A 72,593-parameter model matches a 27.8-million-parameter network, so accurate screening does not require heavy computation and could run at the bedside.
Spectrogram-based deep features outperform handcrafted acoustic baselines, suggesting the learned time–frequency representation captures pain-related structure that manual features miss.
Leave-one-subject-out evaluation indicates the model generalizes across infants rather than memorizing individuals, within the limits of a 31-subject sample.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Going beyond the paper, a decisive follow-up would be to test the model on cry-only segments and on procedure-room audio without any infant cry; the paper does not report that ablation, and it would separate cry-based cues from procedure-context cues.
If the reported accuracy survives a larger multisite sample, a plausible deployment target is a low-power continuous monitor that screens acoustic segments and alerts nurses, rather than replacing NIPS scoring.
The same spectrogram-plus-N-CNN pipeline naturally extends to other neonatal cry categories such as hunger, fatigue, or postoperative pain, since the learned features are not tied to the pain/no-pain dichotomy.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a genuine first in a clinically meaningful niche, but the 96.77% accuracy is probably not measuring what the title claims. The audio windows for pain start right before the heel lance and end after, while the no-pain windows are baseline/recovery, so the classifier has easy access to procedure-related sounds (clapperboard, nurse voices, equipment) that are shared across subjects. LOSO cross-validation removes subject identity, not procedure context. The paper never shows which parts of the spectrogram drive the decision, so "pain cry recognition" and "procedure detection" remain indistinguishable.

What is actually new: as far as I can tell, this is the first deep-learning system applied to neonatal pain classification from cry spectrograms. That's a legitimate new application. They compare three architectures, report LOSO and 10-fold results, and make a fair effort to compare against handcrafted feature baselines on the same NPAD dataset. N-CNN at 72k parameters matching VGG16's accuracy is a nice practical observation. The self-citation for N-CNN is transparent and does not make the result circular.

Soft spots, in rough order: (1) The acoustic confound above is load-bearing. Both the stress-test and the reader's take land on it, and reading the protocol section makes it hard to dismiss. (2) No confidence intervals, sensitivity/specificity, or statistical tests on a 31-subject dataset; point estimates of 96.77% are thin. (3) The input size and augmentation recipe were selected on the same data; no held-out tuning. (4) Comparisons with [18] and [6] come from different datasets. (5) Minor internal inconsistency: the intro credits Pal et al. with 91% pain accuracy, but Table III lists 74.21% for the same method.

That said, the central feasibility story is not crazy. The paper is honest about its dataset and limitations, and the problem (NICU occlusion) is real. It just overclaims "viable alternative to current practice" on the basis of one 31-subject dataset with an uncontrolled protocol. A serious referee should ask for external validation or at least a cry-only segmentation analysis before accepting the accuracy claim. I'd send it to peer review with major revision expected; the confound analysis is the difference between a useful pilot and a misleading one.

Referee Report

3 major / 5 minor

Summary. The paper proposes applying convolutional neural networks to spectrogram images of neonatal crying sounds to classify pain versus no-pain. The authors introduce N-CNN, a lightweight architecture with 72,593 parameters, and compare it against fine-tuned VGG16 and ResNet50 on the NPAD dataset of 31 neonates recorded in a NICU. Using leave-one-subject-out cross-validation, they report 96.77% accuracy and 0.94 AUC for N-CNN and VGG16, with ResNet50 achieving 83.87% accuracy. They also report a 10-fold cross-validation comparison with handcrafted feature baselines in which N-CNN achieves 91.20% accuracy. The paper concludes that automated crying-sound analysis is a viable alternative to current neonatal pain assessment practice.

Significance. If the reported performance reflected pain-specific cry features, the paper would make a valuable contribution: a lightweight, automated pain assessment from sound is clinically relevant, especially when facial/body occlusion prevents visual assessment. The authors are transparent about using a real NICU dataset, use subject-independent leave-one-subject-out evaluation, and compare against several baselines, which are strengths. The N-CNN model is remarkably small relative to VGG16/ResNet50 while matching their reported accuracy, which is an interesting efficiency result. However, the central claim of a viable alternative to current practice is not yet established because the evaluation protocol may allow the classifier to exploit systematic differences between pain and no-pain audio windows that are unrelated to the infant's cry, and because the reported results are point estimates without uncertainty quantification or statistical comparisons.

major comments (3)

[Section III.A and Section IV.A] The audio-event construction creates a systematic acoustic confound. Section III.A states that each extracted audio segment starts immediately before the painful procedure and ends after its completion, while Section IV.A states that nurses provide NIPS scores before, during, and after the procedure, with a clapperboard marking ground-truth events. Consequently, pain windows contain the procedural event itself, including clapperboard transients, nurse instructions, heel-lancing sounds, and any equipment sounds triggered by the procedure, whereas no-pain windows are baseline/recovery periods with mostly ambient NICU noise. The Introduction explicitly acknowledges that the dataset contains nurses' sounds, equipment sounds, and crying sounds of other neonates. Leave-one-subject-out cross-validation removes subject identity but not these procedure-correlated acoustics, so a CNN could achieve high accuracy by detecting procedure-in-progress rather than infant pain. The manuscript provides no analysis of which time-frequency regions drive decisions and no external or non-procedural crying test set. The 96.77% accuracy therefore does not yet support the conclusion that the method assesses pain from crying.
[Section III.A and Section IV.C] The input spectrogram size was selected on the same data used to report performance, which inflates the reported accuracy. The authors state that they experimented with different image sizes and used 120x120 because it achieves the best performance. Similarly, the augmentation recipe (specific frequency shifts and noise levels) is hand-picked without a held-out validation procedure. Because model selection is performed using the test set, the reported 96.77% accuracy and 0.94 AUC are optimistic point estimates. The paper should report the selection procedure, use nested cross-validation or a separate validation set, or otherwise quantify the effect of this selection.
[Table II and Section IV.C] The evaluation lacks uncertainty quantification and statistical inference. With 31 subjects and a binary classification task, an accuracy of 96.77% corresponds to a small number of misclassified subjects or segments, and the paper does not report confidence intervals, sensitivity, specificity, or any statistical test. Consequently, the claim that N-CNN is comparable to VGG16 and superior to ResNet50 is not supported statistically; the differences could arise from chance. The authors should report per-subject results, confidence intervals, and appropriate significance tests, or explicitly frame the results as preliminary feasibility observations.

minor comments (5)

[Section IV.C and Table III] There is an inconsistency between the text and Table III: the text states that the proposed N-CNN achieved 91.20% accuracy and 0.94 AUC, while the table lists 0.91 AUC. Please correct the discrepancy.
[Section II.B and Section III.C] The N-CNN architecture is described as novel but the complete architecture and training parameters are deferred to reference [11]. For a self-contained conference paper, key architectural details should be included, or the novelty claim should be clarified since the architecture appears to have been introduced in that prior work.
[Section IV.A] The paper reports 182 original audio events and 4,914 augmented events but does not report the class distribution (number of pain vs. no-pain segments) or the number of events per subject. This information is important for interpreting the reported accuracy and for assessing class imbalance.
[Table III] The comparison with handcrafted methods in Table III is not apples-to-apples because some baselines were evaluated on different datasets. The text acknowledges this, but the conclusion that the proposed method achieved the highest performance should be more carefully qualified.
[Section III.B] The augmentation procedure assumes that frequency-shifted and noise-added copies of an audio event preserve the original pain/no-pain label. This assumption is plausible for noise addition but less obvious for frequency shifts, especially for infant cry characteristics, and the paper should justify it or cite evidence.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the pain-classification claim rests on a held-out empirical evaluation, not on the cited N-CNN architecture or on any fitted input predicting its own output.

full rationale

The paper's central claim is an empirical accuracy result: N-CNN, VGG16, and ResNet50 are trained on augmented spectrogram images and evaluated with leave-one-subject-out cross-validation on the NPAD dataset. The N-CNN architecture is taken from the authors' prior work [11] and cited transparently, but the reported 96.77% accuracy and 0.94 AUC are measured on held-out subjects rather than derived from that citation. No parameter is fitted to a subset of labels and then used to predict that same subset, and no equation defines the outcome in terms of the input features by construction. The comparison against VGG16, ResNet50, and handcrafted baselines provides independent empirical grounding. The main identified weakness, namely that pain and no-pain audio windows may differ in procedure-related acoustic context (clapperboard, nurse voices, equipment sounds), is a threat to external validity or construct validity of the accuracy measure, not a circularity in the derivation chain. Therefore the appropriate finding is no significant circularity, with a score of 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central performance claim rests on a small, private clinical dataset, nurse-provided labels, a hand-chosen spectrogram size, and an unvalidated augmentation scheme. No new physical entities are introduced.

free parameters (2)

Input spectrogram image size = 120x120 (N-CNN); 224x224 (VGG16/ResNet50)
Selected as best-performing image size in Section III.A; this data-dependent choice affects all reported accuracy values.
Augmentation parameters = f/2, f/3, 2f/3; noise levels 0.01, 0.05, 0.001, 0.005, 0.003, 0.03; 18 combinations
Hand-chosen augmentation recipe in Section III.B with no sensitivity analysis; it determines the 27-fold expanded training set.

assumptions (3)

domain assumption NIPS scores assigned by trained nurses are a valid ground truth for neonatal pain.
Used to label every audio segment (Section IV.A); if NIPS is noisy or biased, the classifier learns labels rather than pain.
domain assumption The spectrogram representation preserves the pain-discriminative information in the cry signal.
All models operate on spectrogram images (Section II.A); no ablation compares raw audio or other representations.
ad hoc to paper Augmented frequency-shifted and noise-added copies of an audio event keep the same pain/no-pain label.
The 27-fold augmentation (Section III.B) assumes label preservation, which is not validated.

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Cite this review

Pith. "Pith review of Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound." pith.science (2026). https://pith.science/paper/VLG6AXXN

@misc{pith2026190902543,
  author       = {Pith},
  title        = {Pith review of: Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VLG6AXXN}},
  note         = {Machine review of arXiv:1909.02543}
}

read the original abstract

Neonatal pain assessment in clinical environments is challenging as it is discontinuous and biased. Facial/body occlusion can occur in such settings due to clinical condition, developmental delays, prone position, or other external factors. In such cases, crying sound can be used to effectively assess neonatal pain. In this paper, we investigate the use of a novel CNN architecture (N-CNN) along with other CNN architectures (VGG16 and ResNet50) for assessing pain from crying sounds of neonates. The experimental results demonstrate that using our novel N-CNN for assessing pain from the sounds of neonates has a strong clinical potential and provides a viable alternative to the current assessment practice.

Figures

Figures reproduced from arXiv: 1909.02543 by the authors.

**Figure 2.** Sample of original pain audio signal and its corre [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Sample of augmented image of the original no-pain [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Sample of augmented image of the original pain audio [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 13 canonical work pages

[18]

Identiﬁcation of pain from infant cry vocalizations using artiﬁcial neural networks (anns),

M. Petroni, A. S. Malowany, C. C. Johnston, and B. J. Stevens, “Identiﬁcation of pain from infant cry vocalizations using artiﬁcial neural networks (anns),” in Applications and Science of Artiﬁcial Neural Networks , vol. 2492. International Society for Optics and Photonics, 1995, pp. 729–738

work page 1995
[6]

Emotion detection from infant facial expressions and cries,

P. Pal, A. N. Iyer, and R. E. Yantorno, “Emotion detection from infant facial expressions and cries,” in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 2. IEEE, 2006, pp. II–II

work page 2006
[1]

Machine-based Multimodal Pain Assessment Tool for Infants: A Review

G. Zamzmi, D. Goldgof, R. Kasturi, Y . Sun, and T. Ashmeade, “Machine-based multimodal pain assessment tool for infants: a re- view,” arXiv preprint arXiv:1607.00331 , 2016

work page Pith review arXiv 2016
[2]

A review of automated pain assessment in infants: Features, classiﬁ- cation tasks, and databases,

G. Zamzmi, R. Kasturi, D. Goldgof, R. Zhi, T. Ashmeade, and Y . Sun, “A review of automated pain assessment in infants: Features, classiﬁ- cation tasks, and databases,” IEEE reviews in biomedical engineering , vol. 11, pp. 77–96, 2018

work page 2018
[3]

A comprehensive and context-sensitive neonatal pain as- sessment using computer vision,

G. Zamzmi, C.-Y . Pai, D. Goldgof, R. Kasturi, T. Ashmeade, and Y . Sun, “A comprehensive and context-sensitive neonatal pain as- sessment using computer vision,” IEEE Transactions on Affective Computing, 2019, early access DOI:10.1109/TAFFC.2019.2926710

arXiv 2019
[4]

Devel- opmental changes in pain expression in premature, full-term, two-and four-month-old infants,

C. C. Johnston, B. Stevens, K. D. Craig, and R. V . Grunau, “Devel- opmental changes in pain expression in premature, full-term, two-and four-month-old infants,” Pain, vol. 52, no. 2, pp. 201–208, 1993

work page 1993
[5]

Procedural pain and brain development in premature newborns,

S. Brummelte, R. E. Grunau, V . Chau, K. J. Poskitt, R. Brant, J. Vinall, A. Gover, A. R. Synnes, and S. P. Miller, “Procedural pain and brain development in premature newborns,” Annals of neurology , vol. 71, no. 3, pp. 385–396, 2012

work page 2012
[7]

Characterization of infant cries using spectral and prosodic features,

R. R. Vempada, B. S. A. Kumar, and K. S. Rao, “Characterization of infant cries using spectral and prosodic features,” in 2012 National Conference on Communications (NCC) . IEEE, 2012, pp. 1–5

work page 2012

Show all 18 references

[8]

Neonatal cry analysis and categorization system via directed acyclic graph support vector machine,

S.-T. Chen, K. Srinivasan, C. Lin, and C.-Y . Chang, “Neonatal cry analysis and categorization system via directed acyclic graph support vector machine,” in Big Data Analytics for Sensor-Network Collected Intelligence. Elsevier, 2017, pp. 205–222

2017
[9]

Multi-channel neural network for assessing neonatal pain from videos,

M. S. Salekin, G. Zamzmi, D. Goldgof, R. Kasturi, T. Ho, and Y . Sun, “Multi-channel neural network for assessing neonatal pain from videos,” arXiv preprint arXiv:1908.09254 , 2019

1908 arXiv
[10]

Automatic detection of cry sounds in neonatal intensive care units by using deep learning and acoustic scene simulation,

M. Severini, D. Ferretti, E. Principi, and S. Squartini, “Automatic detection of cry sounds in neonatal intensive care units by using deep learning and acoustic scene simulation,” IEEE Access , vol. 7, pp. 51 982–51 993, 2019

2019
[11]

Convolutional neural networks for neonatal pain assessment,

G. Zamzmi, R. Paul, M. S. Salekin, D. Goldgof, R. Kasturi, T. Ho, and Y . Sun, “Convolutional neural networks for neonatal pain assessment,” IEEE Transactions on Biometrics, Behavior, and Identity Science , vol. 1, no. 3, pp. 192–200, 2019

2019
[12]

Very deep convolutional networks for large-scale image recognition,

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014

2014 arXiv
[13]

Deep residual learning for image recognition,

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016
[14]

Deep speech: Scaling up end-to-end speech recognition,

A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates et al. , “Deep speech: Scaling up end-to-end speech recognition,” arXiv preprint arXiv:1412.5567, 2014

2014 arXiv
[15]

Analysis of spectrogram image methods for sound event classiﬁcation,

J. Dennis, H. D. Tran, and E. S. Chng, “Analysis of spectrogram image methods for sound event classiﬁcation,” in Fifteenth Annual Conference of the International Speech Communication Association , 2014

2014
[16]

A. V . Oppenheim, Discrete-time signal processing. Pearson Education India, 1999

1999
[17]

Validation of the pain assessment in neonates (pain) scale with the neonatal infant pain scale (nips),

D. Hudson-Barr, B. Capper-Michel, S. Lambert, T. M. Palermo, K. Morbeto, and S. Lombardo, “Validation of the pain assessment in neonates (pain) scale with the neonatal infant pain scale (nips),” Neonatal Network, vol. 21, no. 6, pp. 15–22, 2002

2002

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [18]

Identiﬁcation of pain from infant cry vocalizations using artiﬁcial neural networks (anns),

M. Petroni, A. S. Malowany, C. C. Johnston, and B. J. Stevens, “Identiﬁcation of pain from infant cry vocalizations using artiﬁcial neural networks (anns),” in Applications and Science of Artiﬁcial Neural Networks , vol. 2492. International Society for Optics and Photonics, 1995, pp. 729–738

work page 1995

[2] [6]

Emotion detection from infant facial expressions and cries,

P. Pal, A. N. Iyer, and R. E. Yantorno, “Emotion detection from infant facial expressions and cries,” in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 2. IEEE, 2006, pp. II–II

work page 2006

[3] [1]

Machine-based Multimodal Pain Assessment Tool for Infants: A Review

G. Zamzmi, D. Goldgof, R. Kasturi, Y . Sun, and T. Ashmeade, “Machine-based multimodal pain assessment tool for infants: a re- view,” arXiv preprint arXiv:1607.00331 , 2016

work page Pith review arXiv 2016

[4] [2]

A review of automated pain assessment in infants: Features, classiﬁ- cation tasks, and databases,

G. Zamzmi, R. Kasturi, D. Goldgof, R. Zhi, T. Ashmeade, and Y . Sun, “A review of automated pain assessment in infants: Features, classiﬁ- cation tasks, and databases,” IEEE reviews in biomedical engineering , vol. 11, pp. 77–96, 2018

work page 2018

[5] [3]

A comprehensive and context-sensitive neonatal pain as- sessment using computer vision,

G. Zamzmi, C.-Y . Pai, D. Goldgof, R. Kasturi, T. Ashmeade, and Y . Sun, “A comprehensive and context-sensitive neonatal pain as- sessment using computer vision,” IEEE Transactions on Affective Computing, 2019, early access DOI:10.1109/TAFFC.2019.2926710

arXiv 2019

[6] [4]

Devel- opmental changes in pain expression in premature, full-term, two-and four-month-old infants,

C. C. Johnston, B. Stevens, K. D. Craig, and R. V . Grunau, “Devel- opmental changes in pain expression in premature, full-term, two-and four-month-old infants,” Pain, vol. 52, no. 2, pp. 201–208, 1993

work page 1993

[7] [5]

Procedural pain and brain development in premature newborns,

S. Brummelte, R. E. Grunau, V . Chau, K. J. Poskitt, R. Brant, J. Vinall, A. Gover, A. R. Synnes, and S. P. Miller, “Procedural pain and brain development in premature newborns,” Annals of neurology , vol. 71, no. 3, pp. 385–396, 2012

work page 2012

[8] [7]

Characterization of infant cries using spectral and prosodic features,

R. R. Vempada, B. S. A. Kumar, and K. S. Rao, “Characterization of infant cries using spectral and prosodic features,” in 2012 National Conference on Communications (NCC) . IEEE, 2012, pp. 1–5

work page 2012