Archives of Acoustics, 49, 4, pp. 527–542, 2024
10.24425/aoa.2024.148816

Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation

Jiabao LI
School of Mechanical and Aerospace Engineering, Jilin University
China

Lichi AN
School of Mechanical and Aerospace Engineering, Jilin University
China

Yabing CHENG
School of Mechanical and Aerospace Engineering, Jilin University
China

Haoxiang WANG
School of Mechanical and Aerospace Engineering, Jilin University
China

The sound quality of transmission system noise significantly impacts user experience. This study aims to predict the sound quality of dual-phase Hy-Vo chain transmission system noise using a small sample size. Noise acquisition tests are conducted under various working conditions, followed by subjective evaluations using the equal interval direct one-dimensional method. Objective evaluations are performed using the Mel-frequency cepstral coefficient (MFCC). To understand the impact of the MFCC order and the frame number on prediction accuracy, MFCC feature maps of different specifications are analyzed. The dataset is expanded threefold using fuzzy generation with an appropriate membership degree. The convolutional neural network (CNN) is developed, utilizing MFCC feature maps as inputs and evaluation scores as outputs. Results indicate a positive correlation between the frame number and prediction accuracy, whereas higher MFCC orders introduce redundancy, reducing accuracy. The proposed CNN method outperforms three traditional machine learning approaches, demonstrating superior accuracy and resistance to overfitting.
Keywords: sound quality; dual-phase transmission; Hy-Vo chain; MFCC; fuzzy generation
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Copyright © 2024 The Author(s). This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.

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DOI: 10.24425/aoa.2024.148816