A Deep Convolutional Neural Network Based Approach for Effective Neonatal Cry Classification

Abstract

Background: The cry is the universal language for babies to communicate with others. Infant cry classification is a kind of speech recognition problem that should be treated wisely. In the last few years, it has been gaining its momentum which will be very helpful for the caretaker. Objective: This study aims to develop infant cry classification system predictive model by converting the audio signals into spectrogram image then implementing deep convolutional neural network. It performs end to end learning process and thereby reducing the complexity involved in audio signal analysis and improves the performance using optimization technique. Method: A time frequency-based analysis called Short Time Fourier Transform (STFT) is applied to generate the spectrogram. 256 DFT (Discrete Fourier Transform) points are considered to compute the Fourier transform. A Deep convolutional neural network called AlexNet with few enhancements is done in this work to classify the recorded infant cry. To improve the effectiveness of the above mentioned neural network, Stochastic Gradient Descent with Momentum (SGDM) is used to train the algorithm. Results: A deep neural network-based infant cry classification system achieves a maximum accuracy of 95% in the classification of sleepy cries. The result shows that convolutional neural network with SGDM optimization acquires higher prediction accuracy. Conclusion: Since this proposed work is compared with convolutional neural network with SGD and Naïve Bayes and based on the result, it is implied the convolutional neural network with SGDM performs better than the other techniques.