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

Abstract

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%.