Identification of sloshing noises using convolutional neural network

Siva Teja, Golla and Saurav Vara Prasad, Chennuri and Venkatesham, B. and Sri Rama Murty, K. (2021) Identification of sloshing noises using convolutional neural network. The Journal of the Acoustical Society of America, 149 (5). pp. 3027-3041. ISSN 0001-4966

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Sloshing in fuel tanks has become a new source of noise in hybrid and high-end vehicles in the wake of reduced noise from major sources like the engine. It occurs due to the interactions of fluid inside the tank under various driving conditions of the vehicle. Interactions of fluid with the tank walls cause hit noise, and the fluid-fluid interactions cause splash noise. As the generation mechanism is different, the hit and splash noises demand different noise controlling strategies. Thus, identifying these noises during the design stage is important for implementing effective solutions in designing a quieter fuel tank. This paper presents a convolutional neural network (CNN) based methodology for the identification of sloshing noises under different conditions of fill level, excitation, baffle configuration, etc. Data for training and testing the network are collected using a reciprocating test setup, which facilitates the generation of hit and splash noises in a rectangular tank. The identification accuracy of the features learned by CNN is compared with the hand-crafted features using support vector machines. The applicability of the proposed CNN model is tested for practical scenarios like vehicle braking, where different types of sloshing noises occur in quick succession.

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IITH Creators:
IITH CreatorsORCiD
Saurav Vara Prasad, ChennuriUNSPECIFIED
Venkatesham, B
Item Type: Article
Uncontrolled Keywords: Baffle configurations; Controlling strategies; Driving conditions; Effective solution; Fluid-fluid interaction; Generation mechanism; Identification accuracy; Training and testing; Automobile fuel tanks; Convolution; Excited states; Fuel sloshing; Support vector machines
Subjects: Physics > Mechanical and aerospace
Physics > Mechanical and aerospace > Transportation Science & Technology
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Jun 2021 06:02
Last Modified: 23 Jun 2021 06:02
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