scholarly journals Crossterm-Free Time-Frequency Representation Exploiting Deep Convolutional Neural Network

2021 ◽  
pp. 108372
Author(s):  
Shuimei Zhang ◽  
Md. Saidur Rahman Pavel ◽  
Yimin D. Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Free Time ◽  
Deep Convolutional Neural Network ◽  
Time Frequency ◽  
Frequency Representation

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 Featureless EMG pattern recognition based on convolutional neural network

2019 ◽  
Vol 14 (3) ◽  
pp. 1291
Author(s):  
Too Jing Wei ◽  
Abdul Rahim Bin Abdullah ◽  
Norhashimah Binti Mohd Saad ◽  
Nursabillilah Binti Mohd Ali ◽  
Tengku Nor Shuhada Binti Tengku Zawawi
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Frequency Distribution ◽  
Classification Accuracy ◽  
Time Frequency ◽  
Frequency Representation ◽  
Emg Pattern Recognition ◽  
Time Frequency Distribution

In this paper, the performance of featureless EMG pattern recognition in classifying hand and wrist movements are presented. The time-frequency distribution (TFD), spectrogram is employed to transform the raw EMG signals into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the images without the need of manual feature extraction. The performance of CNN with different number of convolutional layers is examined. The proposed CNN models are evaluated using the EMG data from 10 intact and 11 amputee subjects through the publicly access NinaPro database. Our results show that CNN classifier offered the best mean classification accuracy of 88.04% in recognizing hand and wrist movements.

scholarly journals Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

2021 ◽  
Vol 11 (16) ◽  
pp. 7575
Author(s):  
Cong Dai Nguyen ◽  
Zahoor Ahmad ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Vibration Signal ◽  
Deep Convolutional Neural Network ◽  
Variable Speed ◽  
Time Frequency ◽  
Related Information ◽  
Imaging Approach ◽  
Adaptive Denoising

This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful fault-related information is often overwhelmed by random interference noises. Furthermore, the varying speed of gearboxes makes it difficult to distinguish the fault-related frequencies from the interference noises. To obtain a noise-free signal for extraction of fault-related information under variable speed conditions, first, a new localized adaptive denoising technique (LADT) is applied to the vibration signal. The new localized adaptive denoising technique results in optimized vibration sub-bands with negligible background noise. To obtain fault-related information, the wavelet-based vibration imaging approach (WVI) is applied to the denoised vibration signal. The wavelet-based vibration imaging approach decomposes the vibration signal into different time–frequency scales, these scales are reflected by a two-dimensional image called a scalogram. The scalograms obtained from the wavelet-based vibration imaging approach are provided as an input to the deep convolutional neural network architecture (DCNA) for extraction of discriminant features and classification of multi-degree tooth faults (MDTFs) in a gearbox under variable speed conditions. The proposed scheme outperforms the already existing state-of-the-art gearbox fault diagnosis methods with the highest classification accuracy of 100%.

scholarly journals Identification of Microrecording Artifacts with Wavelet Analysis and Convolutional Neural Network: An Image Recognition Approach

2019 ◽  
Vol 19 (5) ◽  
pp. 222-231 ◽  
Author(s):  
Ondřej Klempíř ◽  
Radim Krupička ◽  
Eduard Bakštein ◽  
Robert Jech
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clinical Analysis ◽  
Substantial Improvement ◽  
Time Frequency ◽  
Microelectrode Recordings ◽  
Frequency Representation ◽  
High Level ◽  
Accepted Form ◽  
Deep Brain

Abstract Deep brain stimulation (DBS) is an internationally accepted form of treatment option for selected patients with Parkinson’s disease and dystonia. Intraoperative extracellular microelectrode recordings (MER) are considered as the standard electrophysiological method for the precise positioning of the DBS electrode into the target brain structure. Pre-processing of MERs is a key phase in clinical analysis, with intraoperative microelectrode recordings being prone to several artifact groups (up to 25 %). The aim of this methodological article is to provide a convolutional neural network (CNN) processing pipeline for the detection of artifacts in an MER. We applied continuous wavelet transform (CWT) to generate an over-complete time–frequency representation. We demonstrated that when attempting to find artifacts in an MER, the new CNN + CWT provides a high level of accuracy (ACC = 88.1 %), identifies individual classes of artifacts (ACC = 75.3 %) and also offers artifact time onset detail, which can lead to a reduction in false positives/negatives. In summary, the presented methodology is capable of identifying and removing various artifacts in a comprehensive database of MER and represents a substantial improvement over the existing methodology. We believe that this approach will assist in the proposal of interesting clinical hypotheses and will have neurologically relevant effects.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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