Object classification with convolution neural network based on the time-frequency representation of their echo

The development of acoustic scenes recognition systems has been a topic of extensive research due to its applications in several fields of science and engineering. This paper proposes an environmental system in which firstly a time-frequency representation is obtained using the Continuous Wavelet Transform (CWT). The time frequency representation is then represented as a color image using the Viridis color map, which is then inserted into a Deep Neural Network (DNN) to carry out the classification task. Evaluation results using several public data bases show that proposed scheme provides a classification performance better than the performance provided by other previously proposed schemes.

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

Signal Processing ◽

10.1016/j.sigpro.2021.108372 ◽

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

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Training Network Design Based on Convolution Neural Network for Object Classification in few class problem

The Journal of the Korean Institute of Information and Communication Engineering ◽

10.6109/jkiice.2017.21.1.144 ◽

2017 ◽

Vol 21 (1) ◽

pp. 144-150 ◽

Cited By ~ 2

Author(s):

Su-chang Lim ◽

Seung-Hyun Kim ◽

Yeon-Ho Kim ◽

Do-yeon Kim

Keyword(s):

Neural Network ◽

Network Design ◽

Object Classification ◽

Convolution Neural Network

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Infrared Object Classification with a HybridOptical Convolution Neural Network

Applied Optics ◽

10.1364/ao.427973 ◽

2021 ◽

Author(s):

Jianbo Chen ◽

Jennifer Talley ◽

Kevin Kelly

Keyword(s):

Neural Network ◽

Object Classification ◽

Convolution Neural Network

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THICKNESS DETERMINATION OF A PLATE WITH VARYING THICKNESS USING AN ARTIFICIAL NEURAL NETWORK FOR TIME-FREQUENCY REPRESENTATION OF LAMB WAVES

10.1063/1.3114313 ◽

2009 ◽

Cited By ~ 2

Author(s):

C. Treesatayapun ◽

A. Baltazar ◽

A. Balvantin ◽

J.-Y. Kim ◽

Donald O. Thompson ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Lamb Waves ◽

Time Frequency ◽

Varying Thickness ◽

Thickness Determination ◽

Frequency Representation ◽

Artificial Neural

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Featureless EMG pattern recognition based on convolutional neural network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v14.i3.pp1291-1297 ◽

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.

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