Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Автор: Arshia Foroozan Yazdani, Ali Bozorgi Mehr, Iman Showkatyan, Amin Hashemi, Mohsen Kakavand

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 1 vol.13, 2021 года.

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The present study, the main idea of which was based on one of the questions of I.P.T.2018 competition, aimed to develop a high-precision relationship between the fluid temperature and the sound produced when colliding with different surfaces, by creating a data collection tool. In fact, this paper was provided based on a traditional phenomenological project using the well-known deep neural networks, in order to achieve an acceptable accuracy in this project. In order to improve the quality of the paper, the data were analyzed in two ways: I. Using the images of data spectrogram and the known V.G.G.16 network. II. Applying the data audio signal and a convolutional neural network (C.N.N.). Finally, both methods have obtained an acceptable precision above 85%.

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Fluid temperature, data spectrogram, V.G.G.16 (Visual Geometry Group), C.N.N (Convolutional Neural Network)., the environmental sounds, sound classification, physical-computational research, Deep learning

Короткий адрес: https://sciup.org/15017382

IDR: 15017382   |   DOI: 10.5815/ijigsp.2021.01.03

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