Nam Jaehyeon, Kim Seokbeom, Ko Dongshin
AI & Mechanical System Center, Institute for Advanced Engineering, Youngin-si 17180, Republic of Korea.
Sensors (Basel). 2024 Aug 8;24(16):5128. doi: 10.3390/s24165128.
This study researched the prediction of the BSR noise evaluation quantitative index, Loudness N10, for sound sources with noise using statistics and machine learning. A total of 1170 data points was obtained from 130 automotive seats measured at 9-point positions, with Gaussian noise integrated to construct synthetic sound data. Ten physical quantities related to sound quality and sound pressure were used and defined as dB and fluctuation strength, considering statistical characteristics and Loudness N10. BSR quantitative index prediction was performed using regression analysis with K-fold cross-validation, DNN in hold-out, and DNN in K-fold cross-validation. The DNN in the K-fold cross-validation model demonstrated relatively superior prediction accuracy, especially when the data quantity was relatively small. The results demonstrate that applying machine learning to BSR prediction allows for the prediction of quantitative indicators without complex formulas and that specific physical quantities can be easily estimated even with noise.
本研究运用统计学和机器学习方法，对带噪声声源的BSR噪声评估定量指标响度N10进行了预测。从130个汽车座椅在9个位置测量得到总共1170个数据点，并通过整合高斯噪声构建合成声音数据。考虑到统计特性和响度N10，使用了10个与声音质量和声压相关的物理量，并将其定义为分贝和波动强度。采用K折交叉验证的回归分析、留出法中的深度神经网络（DNN）以及K折交叉验证中的DNN进行BSR定量指标预测。K折交叉验证模型中的DNN表现出相对较高的预测准确率，尤其是在数据量相对较小时。结果表明，将机器学习应用于BSR预测，无需复杂公式即可预测定量指标，并且即使存在噪声也能轻松估计特定物理量。
