Automatic Baby Cry Detector with sleep music player (ABCD)
Abstract
In today’s world, our lives have become more
dependent on technology. One problem which caught our eyes
is when parents have to leave their wards (aged between 3
months to 2 years) alone due to some essential tasks for a short
duration, the baby goes unmonitored. Normally, parents do
this by leaving their child asleep. In many cases, the child
wakes up and starts to cry. In absence of loved ones, babies
need immediate calmness relief. A baby’s cry can be
characterized by its natural periodic tone and the change of its
voice. This study of sound recognition involves feature
extraction and classification by determining the sound pattern.
The solution to this problem is, Automatic Baby Cry Detector
with sleep music player (ABCD), which is programmed on
Python and the heart of this device is an Audio learning model.
Of this model, a Deep Learning algorithm works by first
extracting MFCCs of the filtered audio. Then, these sound
features (extracted by MFCCs) are predicted by the trained
Model. This trained model identifies the Baby Cry by
classifying audio using K-Nearest Neighbor (k-NN) method.
Based on the probability confirmer (for model accuracy), this
model confirms a baby’s cry. Then the program can further
play a lullaby (programmable) and also alert the parents by
sending a notification on their phone.
Keywords
Full Text:
PDFReferences
Barakova, E. I. (2016). New parents and media use: Associations
between reading online, journalistic and personal sources, and
parental competence. Journal of Child and Family Studies, 25(3),
-868.
Murray, L., & Trevarthen, C. (1985). Emotional regulation of
interactions between two-month-olds and their mothers. In Social
perception in infants (pp. 177-197). Springer, Boston, MA.
Anuj Sable. (2021). “Fourier transforms, STFTs, Mel scale and
Cepstrums with several spectral features”, In Paperspace Blog -
Introduction to Audio Analysis and Processing, 15-min read.
Chang, C. C., Huang, Y. S., Chen, S. A., & Chang, Y. J. (2008).
Automatic infant cry detection and recognition using wavelet packets
and support vector machines. Computer Methods and Programs in
Biomedicine, 91(1), 1-12.
Indra Adji Sulistijono, Renita Chulafa Urrosyda, and Zaqiatud
Darojah. (2016). “Mel-Frequency Cepstral Coefficient MFCC for
Music Feature Extraction for the Dancing Robot Movement
Decision.”, In Proceedings, Part II, of the 9th International
Conference on Intelligent Robotics and Applications - Volume 9835
(ICIRA 2016). Springer-Verlag, Berlin, Heidelberg, pp. 283–294.
V. V Bhagat Patil and P. V. M. Sardar. (2014). “An Automatic
Infant’s Cry Detection Using Linear Frequency Cepstral Coefficients
(LFCC)”, vol. 5, no. 12, pp.1379–1383.
Chen, X., Liu, Y., & Zhang, Z. (2016). “Detection of infant cry onset
using Teager energy operator.”, Biomedical Signal Processing and
Control, 24, 97-103.
Lee, J., Lee, J., & Kim, J. (2017). “Ensemble classification of infant
cry signals for the diagnosis of the cry-induced illnesses.”,
Biomedical Signal Processing and Control, 33, 142-152.
Jaiswal, S., Parisi, L., & Sharma, M. (2019). “An approach to infant
cry analysis using acoustic and visual features.”, Biomedical Signal
Processing and Control, 54, 101608.
Gopalan, R., & Dharshana, L. S. (2019). “Baby Cry Classification
Using Deep Learning Techniques.”, In 2019 International Conference
on Intelligent Computing, Instrumentation and Control Technologies
(ICICICT) (pp. 6-10). IEEE.
Oliphant, T. E. (2006). “A guide to NumPy.”, USA: Trelgol
Publishing.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D. & van der Walt, S. J. (2020). “SciPy 1.0: fundamental
algorithms for scientific computing in Python.”, Nature Methods
(3), 261-272.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B.,
Grisel, O. & Vanderplas, J. (2011). “Scikit-learn: Machine learning in
Python. Journal of Machine Learning Research”, 12(Oct), 2825-2830.
McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M.,
Battenberg, E. & Yamamoto, T. (2020). “librosa: Audio and music
signal analysis in Python.” In Proceedings of the 14th Python in
Science Conference (pp. 18-25).
Badiu, M. (2019). “Infant cry detection and classification using mel-
frequency cepstral coefficients and neural networks.” IEEE Access, 7,
-109329.
Sahidullah, M., M. R., M. A., I., M. N. & Ahmed, M. M. (2018).
“Speech Recognition Using MFCC and DTW.”, International Journal
of Computer Applications, 178(4), 20-24.
M. S., & Mostofa, M. G. (2015). “A Comparative Study of Mel-
Frequency Cepstral Coefficients (MFCC), Perceptual Linear
Prediction (PLP) and Linear Predictive Cepstral Coefficients (LPCC)
for Speech Recognition.”, International Journal of Advanced
Computer Science and Applications, 6(10), 193-198.
Kim, J., Song, J., & Kim, Y. (2017). “Analysis of MFCC Variability
in Speaker Recognition Systems.”, International Journal of Advanced
Science and Technology, 107, 53-60.
Sharma, A., Dutta, S., & Choudhury, S. (2017). “Classification of
Infant Cries Using Mel-Frequency Cepstral Coefficients (MFCC).”,
Journal of Signal Processing Systems, 88(1), 1-10.
Kumar, S. V., Reddy, S. V., & Ravi, S. (2020). “Emotion Recognition
in Speech Signals using Wavelet Transform and MFCC Features.”,
International Journal of Speech Technology, 23(2), 317-327.
Muzaffar, H., et al. (2021). “Performance analysis of machine
learning algorithms for infant cry classification.”, IEEE Access, 9,
-25053.
N. Yuan, S. Liu, J. Han, and X. Zhang, “A study of automatic
classification of infant cry signals,” in 2017 IEEE International
Conference on Signal and Image Processing Applications (ICSIPA),
, pp. 342-347.
X. Zhang, H. Li, and G. Li, “Analysis of infant cry based on sound
feature extraction and classification,” in 2019 IEEE 2nd International
Conference on Information and Computer Technologies (ICICT),
, pp. 185-189.
LeCun, Y., Bengio, Y., & Hinton, G. (1998). “Deep learning.”,
Nature, 521(7553), 436-444.
Rakhlin, A., Shvets, A., Iglovikov, V. I., & Kalinin, A. A. (2018).
“Deep convolutional neural networks for breast cancer histology
image analysis.”, In Proceedings of the IEEE International
Symposium on Biomedical Imaging (pp. 328-331).
Zhao, J., Li, L., & Sun, M. (2019). “Facial expression recognition
based on convolutional neural network.”, Journal of Physics:
Conference Series, 1182(2), 022041.
Karol J. Piczak. (14th Feb., 2023). “ESC-50: Dataset for
Environmental Sound Classification”, GitHub.
https://github.com/karolpiczak/ESC-50
Varsha Dange, Tejaswini Bhosale. (2022). “Automatic Detection of
Baby Cry using Machine Learning with Self Learning Music Player
System for Soothing”, in IJRASET, 41433.
DOI: https://doi.org/10.37591/joma.v10i1.7009
Refbacks
- There are currently no refbacks.