← Back to VOLUME 13, ISSUE 5, MAY 2024
This work is licensed under a Creative Commons Attribution 4.0 International License.
Infant Cry Analysis
Viraj Malusare, Aneesh Mote, Amar Yele, Asif Shaikh, Asst. Prof. Nitisha Rajgure
DOI: 10.17148/IJARCCE.2024.13584
Abstract:
In this research, we have proposed a machine learning model that works on Random Forest Classifier, which extracts the MFCCs(Mel-Frequency Cepstral Coefficients) from baby cries and utilizes these features for predictions such as hungry, belly-pain, burping, tired and discomfort. This research can help the parents, caregivers to determine the exact reason behind the crying baby and suggesting the necessary actions to be taken further depending upon the baby cry.Keywords:
MFCCs(Mel-Frequency Cepstral Coefficients), FFT(Fast Fourier Transform), ML(Machine Learning), DL(Deep Learning), LSTM(Long Short Term Memory).π 25 views
This work is licensed under a Creative Commons Attribution 4.0 International License.How to Cite:
[1] Viraj Malusare, Aneesh Mote, Amar Yele, Asif Shaikh, Asst. Prof. Nitisha Rajgure, βInfant Cry Analysis,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2024.13584