An Automatic Instrument Recognition Approach Based on Deep Convolutional Neural Network

(E-pub Ahead of Print)

Author(s): Jiangyan Ke, Rongchuan Lin, Ashutosh Sharma*

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

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Background: This paper presents an automatic instrument recognition method highlighting the deep learning aspect of instrument identification in order to advance the automatic process of video monitoring remotely equipment of substation.

Methodology: This work utilizes the Scale Invariant Feature Transform approach (SIFT) and the Gaussian difference model for instrument positioning while proposing a design scheme of instrument identification system.

Results: The experimental outcomes obtained proves that the proposed system is capable of automatically recognizing a modest graphical interface and study independently while improving the operation effectiveness of appliance and realizing the purpose of spontaneous self-check. The proposed approach is applicable for musical instrument recognition and it provides 92% of the accuracy rate, 87.5% precision value and recall rate of 91.2%.

Conclusion: The comparative analysis with other state of the art methods justifies that the proposed deep learning based music recognition method outperforms the other existing approaches in terms of accuracy, thereby providing a practicable music instrument recognition solution.

Keywords: Deep learning, Instrument identification, SIFT, Neural network, Deep convolutional neural network.

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Article Details

Published on: 22 March, 2021
(E-pub Ahead of Print)
DOI: 10.2174/2352096514666210322155008
Price: $95

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