A Deep Convolutional Neural Network based Approach for Effective Neonatal cry Classification

2020 ◽  
Vol 13 ◽  
Author(s):  
K Ashwini ◽  
P M Durai Raj Vincent
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Convolutional Neural Network ◽  
Classification System ◽  
Optimization Technique ◽  
Audio Signal ◽  
Deep Convolutional Neural Network ◽  
Stochastic Gradient Descent ◽  
Infant Cry ◽  
Time Frequency

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.

scholarly journals Tool Wear Monitoring with Vibration Signals Based on Short-Time Fourier Transform and Deep Convolutional Neural Network in Milling

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhiwen Huang ◽  
Jianmin Zhu ◽  
Jingtao Lei ◽  
Xiaoru Li ◽  
Fengqing Tian
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Tool Wear Monitoring ◽  
Short Time Fourier Transform ◽  
Vibration Signals ◽  
Wear Monitoring ◽  
Short Time

Tool wear monitoring is essential in precision manufacturing to improve surface quality, increase machining efficiency, and reduce manufacturing cost. Although tool wear can be reflected by measurable signals in automatic machining operations, with the increase of collected data, features are manually extracted and optimized, which lowers monitoring efficiency and increases prediction error. For addressing the aforementioned problems, this paper proposes a tool wear monitoring method using vibration signal based on short-time Fourier transform (STFT) and deep convolutional neural network (DCNN) in milling operations. First, the image representation of acquired vibration signals is obtained based on STFT, and then the DCNN model is designed to establish the relationship between obtained time-frequency maps and tool wear, which performs adaptive feature extraction and automatic tool wear prediction. Moreover, this method is demonstrated by employing three tool wear experimental datasets collected from three-flute ball nose tungsten carbide cutter of a high-speed CNC machine under dry milling. Finally, the experimental results prove that the proposed method is more accurate and relatively reliable than other compared methods.

scholarly journals Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chao Fu ◽  
Qing Lv ◽  
Hsiung-Cheng Lin
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Fast Fourier Transform ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Batch Normalization ◽  
Fault Information

It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).

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