Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation
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 subjec...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2024-10-01
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| Series: | Archives of Acoustics |
| Subjects: | |
| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/3995 |
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| author | Jiabao LI Lichi AN Yabing CHENG Haoxiang WANG |
| author_facet | Jiabao LI Lichi AN Yabing CHENG Haoxiang WANG |
| author_sort | Jiabao LI |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c6d50a7256364751a2468cdf0a7012ff |
| institution | Kabale University |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-c6d50a7256364751a2468cdf0a7012ff2025-08-20T03:57:47ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2024-10-0149410.24425/aoa.2024.148816Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy GenerationJiabao LI0Lichi AN1Yabing CHENG2Haoxiang WANG3School of Mechanical and Aerospace Engineering, Jilin UniversitySchool of Mechanical and Aerospace Engineering, Jilin UniversitySchool of Mechanical and Aerospace Engineering, Jilin UniversitySchool of Mechanical and Aerospace Engineering, Jilin UniversityThe 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.https://acoustics.ippt.pan.pl/index.php/aa/article/view/3995sound qualitydual-phase transmissionHy-Vo chainMFCCfuzzy generation |
| spellingShingle | Jiabao LI Lichi AN Yabing CHENG Haoxiang WANG Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation Archives of Acoustics sound quality dual-phase transmission Hy-Vo chain MFCC fuzzy generation |
| title | Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation |
| title_full | Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation |
| title_fullStr | Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation |
| title_full_unstemmed | Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation |
| title_short | Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation |
| title_sort | sound quality prediction method of dual phase hy vo chain transmission system based on mfcc cnn and fuzzy generation |
| topic | sound quality dual-phase transmission Hy-Vo chain MFCC fuzzy generation |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/3995 |
| work_keys_str_mv | AT jiabaoli soundqualitypredictionmethodofdualphasehyvochaintransmissionsystembasedonmfcccnnandfuzzygeneration AT lichian soundqualitypredictionmethodofdualphasehyvochaintransmissionsystembasedonmfcccnnandfuzzygeneration AT yabingcheng soundqualitypredictionmethodofdualphasehyvochaintransmissionsystembasedonmfcccnnandfuzzygeneration AT haoxiangwang soundqualitypredictionmethodofdualphasehyvochaintransmissionsystembasedonmfcccnnandfuzzygeneration |