极限梯度提升声品质预测模型在车内噪声主动控制中的运用

Application of extreme gradient boosting sound quality prediction model in active control of interior noise

针对特种车车内噪声声品质提升问题,利用极限梯度提升(XGBoost)算法建立声品质预测模型,模型预测值与实际主观评价值的平均相对误差为2.43%,分析得到客观参数对主观分数的影响权重;针对车内噪声非线性、非平稳性的特点,提出一种基于经验模态分解(Empirical Mode Decomposition,EMD)和滤波-x最小均方(Filtered-x Least Mean Square,FxLMS)算法相结合的主动控制方法,预测模型结果表明,主观分数提升2.11,提升幅度为26.6%。此方法对特种车内噪声非线性、非平稳性具有良好的控制效果,能有效改善车内声品质。

Aiming at improving the sound quality of the interior noise of special vehicles,the XGBoost algorithm is used to estab-lish a sound quality prediction model.The average relative error between the predicted value of the model and the actual subjective evaluation value is 2.43%.At the same time,according to the nonlinear and non-stationary characteristics of interior noise,an algo-rithm based on Empirical Mode Decomposition(EMD)and Filtered-x Least Mean Square(FxLMS)algorithm is proposed.Based on the active control method,the results of the prediction model show that subjective irritability is optimized by 2.11,with an improvement rate of 26.6%.This method has a good control effect on the nonlinearity and non-stationarity of special vehicle in-terior noise,and can effectively improve the interior sound quality.