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Proceeding Paper

Inherent Emotional Feature Extraction of Neonatal Cry †

Center for Data Mining and Systems Biology, College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Presented at the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY, Gothenburg, Sweden, 12–16 June 2017.
Proceedings 2017, 1(3), 140; https://doi.org/10.3390/IS4SI-2017-04005
Published: 9 June 2017

Abstract

:
Mining the inherent emotional feature of life is of great significance, and the method to extract the inherent emotional feature with a small number of samples is explored based on neonatal cry in this study. The minimum embedding dimension is taken as the nonlinear feature representing nervous system activity and emotion, and is also analyzed at multiple scales. It is found that the minimum embedding dimension of pain cries is higher than that of sad cries, and has a certain change rule in different frequency bands. The results are consistent with related emotional research of brain nerve activity and the characteristics of the pain cry, and may help in the study of information ecology of the brain in different emotions.

1. Introduction

How to extract inherent emotional feature of life with a small number of samples? Due to the purity of neonatal model, neonatal cry is selected. And the nonlinear method is used to explore the small-sample-set way of inherent emotional feature extraction in this study.

2. Materials and Methods

Individuals have different complexity in different emotional states. In this study, 150 labeled segments of neonatal cry audio are downloaded from freesound.org, including pain, angry, hunger and sad labeled cries. The minimum embedding dimensions of the cries are extracted by Cao’s method to reflect the complexity of the system, as a whole and in each frequency bands after wavelet decomposition by the db7 wavelet.

3. Results

The minimum embedding dimensions of the cries due to different reasons are different, as is shown in Table 1. The minimum embedding dimension of pain cries is the highest, and that of sad cries is the lowest.
The distribution of extracted minimum embedding dimensions of 44 segments of pain cries and 31 segments of sad cries is shown in Figure 1, with two high probabilities around 11 and 10 respectively. Thus, it can be deduced that the complexity of the individual nervous system in the state of pain is higher than that in the state of sadness.
The averaged minimum embedding dimensions of pain cries has a large decrease in the d6 frequency band, as is shown in Figure 2, which can indicate that the systematicness of the detail signals of pain cries shows a significant decrease in the d6 frequency band.

4. Discussion

For both positive and negative emotions, there are different patterns of activity in the brain [1]. Activation and pleasantness are dimensions of the conscious experience closely related to the nervous system [2,3]. Individuals are in a state of tension and alertness during pain [4], so in the two-dimensional structure of emotion, the activation of the nervous system is high in pain, and relatively low in the sad state. Pain causes increased activity in the nervous system, leading to increased complexity of the individual life system [5,6,7]. And sadness causes reduced activity in the brain and reduced complexity [8,9,10]. This is consistent with the results obtained in this study. This consistency indicates that the minimum embedding dimension of cries can be used as the feature representing the individual emotional state and the complexity of the nervous system activity.
The crying signal produced by pain begins with a strong and long high pitched pronunciation [11,12], ending in a melodic signal. The initial strong and high pitched cry may be regarded as a kind of warning signal. So the minimum embedding dimension decreases of the pain signals in the d6 frequency band may be due to the increase of the warning signals contained.
By mining the inherent features of neonatal cries, it may help in the study of information ecology of the brain in different emotions. Besides, the machine learning with small sample set may be easier if some knowledge-based rules are set, considering the benefit that the system complexity bring to the inherent emotional features extraction.

5. Conclusions

In this study, the nonlinear method is used for mining the features of neonatal cries, which can be used to represent the state of the human body. It is found that the minimum embedding dimensions extracted from cries caused by different factors are different. The minimum embedding dimension of pain cries is higher than that of sad cries. This finding is consistent with previous studies of emotion and nervous systems. The minimum embedding dimension of cry signals in different frequency bands is extracted. The multi-scale minimum embedding dimension of cries in pain is found to be changed under a certain rule. This is consistent with previous studies of pain-related cries that contain a strong pronunciation. It is suggested that the minimum embedding dimension of cries can be used as a feature to characterize the emotional state of the individual and the complexity of the nervous system. It is also proved that the inherent emotional feature, consistent with emotional studies, is extractable based on a small number of samples by the nonlinear method.

Author Contributions

Jun Meng and Ximeng Zhao conceived and designed the experiments; Ximeng Zhao and Wenyuan Xu performed the experiments; Jun Meng and Ximeng Zhao analyzed the data; Ximeng Zhao and Wenyuan Xu wrote the paper.

Acknowledgments

This work is supported by Science and Technology Program of Zhejiang, China (No. 2017C31079).

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

References

  1. Kassam, K.S.; Markey, A.R.; Cherkassky, V.L.; Loewenstein, G.; Just, M.A. Identifying emotions on the basis of neural activation. PLoS ONE 2013, 8, e66032. [Google Scholar] [CrossRef] [PubMed]
  2. Barrett, L.F.; Russell, J.A. The Structure of Current Affect: Controversies and Emerging Consensus. Curr. Dir. Psychol. Sci. 1999, 8, 10–14. [Google Scholar] [CrossRef]
  3. Russell, J.A. Core affect and the psychological construction of emotion. Psychol. Rev. 2003, 110, 145. [Google Scholar] [CrossRef] [PubMed]
  4. Grunau, R.V.E.; Craig, K.D. Pain expression in neonates: Facial action and cry. Pain 1987, 28, 395–410. [Google Scholar] [CrossRef]
  5. Segerdahl, A.R.; Mezue, M.; Okell, T.W.; Farrar, J.T.; Tracey, I. The dorsal posterior insula subserves a fundamental role in human pain. Nat. Neurosci. 2015, 18, 499–500. [Google Scholar] [CrossRef] [PubMed]
  6. Franck, L.S.; Greenberg, C.S.; Stevens, B. Pain assessment in infants and children. Pediatr. Clin. N. Am. 2000, 47, 487–512. [Google Scholar] [CrossRef]
  7. Navratilova, E.; Porreca, F. Reward and motivation in pain and pain relief. Nat. Neurosci. 2014, 17, 1304–1312. [Google Scholar] [CrossRef] [PubMed]
  8. Lee, B.-T.; Seok, J.-H.; Lee, B.-C.; Cho, S.W.; Yoon, B.-J.; Lee, K.-U.; Chae, J.-H.; Choi, I.-G.; Ham, B.-J. Neural correlates of affective processing in response to sad and angry facial stimuli in patients with major depressive disorder. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2008, 32, 778–785. [Google Scholar] [CrossRef] [PubMed]
  9. Keedwell, P.A.; Andrew, C.; Williams, S.C.R.; Brammer, M.J.; Phillips, M.L. A Double Dissociation of Ventromedial Prefrontal Cortical Responses to Sad and Happy Stimuli in Depressed and Healthy Individuals. Biol. Psychiatry 2005, 58, 495–503. [Google Scholar] [CrossRef] [PubMed]
  10. Mitterschiffthaler, M.T.; Fu, C.H.Y.; Dalton, J.A.; Andrew, C.M.; Williams, S.C.R. A functional MRI study of happy and sad affective states induced by classical music. Hum. Brain Mapp. 2007, 28, 1150–1162. [Google Scholar] [CrossRef] [PubMed]
  11. Johnston, C.C.; Strada, M.E. Acute pain response in infants: A multidimensional description. Pain 1986, 24, 373–382. [Google Scholar] [CrossRef]
  12. Wolff, P.H. The natural history of crying and other vocalizations in early infancy. Determ. Infant Behav. 1969, 4, 81–109. [Google Scholar]
Figure 1. (a) The histogram and the normal probability density function of minimum embedding dimension of 44 pain-labeled and 31 sad-labeled audio data segments; (b) The bounded histogram of minimum embedding dimension of 44 pain-labeled and 31 sad-labeled audio data segments. The minimum embedding dimensions of pain cries and sad cries have high distribution probabilities around 11 and 10 respectively.
Figure 1. (a) The histogram and the normal probability density function of minimum embedding dimension of 44 pain-labeled and 31 sad-labeled audio data segments; (b) The bounded histogram of minimum embedding dimension of 44 pain-labeled and 31 sad-labeled audio data segments. The minimum embedding dimensions of pain cries and sad cries have high distribution probabilities around 11 and 10 respectively.
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Figure 2. (a) Averaged minimum embedding dimensions of the approximation signals of cries in different frequency bands and different states; (b) Averaged minimum embedding dimensions of the detail signals of cries in different frequency bands and different state. The averaged minimum embedding dimensions of approximation signals of pain cries are universally higher than that of sad cries, while those of pain cries’ detail signals show a large decrease in the d6 band, corresponding to the frequency band between 0.34 KHz and 0.69 KHz.
Figure 2. (a) Averaged minimum embedding dimensions of the approximation signals of cries in different frequency bands and different states; (b) Averaged minimum embedding dimensions of the detail signals of cries in different frequency bands and different state. The averaged minimum embedding dimensions of approximation signals of pain cries are universally higher than that of sad cries, while those of pain cries’ detail signals show a large decrease in the d6 band, corresponding to the frequency band between 0.34 KHz and 0.69 KHz.
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Table 1. Averaged minimum embedding dimensions for 150 labeled segments of neonatal cry audio.
Table 1. Averaged minimum embedding dimensions for 150 labeled segments of neonatal cry audio.
LabelPainAngryHungerSad
Number of segments31482744
Averaged minimum embedding dimensions11.0010.3710.199.81
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MDPI and ACS Style

Zhao, X.; Meng, J.; Xu, W. Inherent Emotional Feature Extraction of Neonatal Cry. Proceedings 2017, 1, 140. https://doi.org/10.3390/IS4SI-2017-04005

AMA Style

Zhao X, Meng J, Xu W. Inherent Emotional Feature Extraction of Neonatal Cry. Proceedings. 2017; 1(3):140. https://doi.org/10.3390/IS4SI-2017-04005

Chicago/Turabian Style

Zhao, Ximeng, Jun Meng, and Wenyuan Xu. 2017. "Inherent Emotional Feature Extraction of Neonatal Cry" Proceedings 1, no. 3: 140. https://doi.org/10.3390/IS4SI-2017-04005

APA Style

Zhao, X., Meng, J., & Xu, W. (2017). Inherent Emotional Feature Extraction of Neonatal Cry. Proceedings, 1(3), 140. https://doi.org/10.3390/IS4SI-2017-04005

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