Personalized HRTF Prediction Based on Light GBM Using Anthropometric Data  

作　　者：Yinliang Qiu Jing Wang Zhiyu Li 

机构地区：[1]School of Information and Electronics,Beijing Institute of Technology,Beijing 100876,China

出　　处：《China Communications》2023年第6期166-177,共12页中国通信（英文版）

基　　金：supported by the cooperation between BIT and Ericsson;partially supported by the National Natural Science Foundation of China under Grants No.62071039。

摘　　要：This paper proposes a personalized headrelated transfer function(HRTF)prediction method based on Light GBM using anthropometric data.Considering the overfitting problems of the current training-based prediction methods,we use Light GBM and a specific network structure to prevent over-fitting and enhance the prediction performance.By decomposing and combining the data to be predicted,we set up 90 Light GBM models to separately predict the 90instants of HRTF in log domain.At the same time,the method of 10-fold cross-validation is used to score the accuracy of the model.For models with scores below 80 points,Bayesian optimization is used to adjust model hyperparameters to obtain a better model structure.The results obtained by Light GBM are evaluated with spectral distortion(SD)which can show the fitting error between the prediction and the original data.The mean SD values of both ears on the whole test set are 2.32 d B and 2.28 d B respectively.Compared with the non-linear regression method and the latest method,SD value of Light GBM-based method relatively decreases by 83.8%and 48.5%.

关 键 词：personalized HRTF anthropometric data LightGBM OVER-FITTING 

分 类 号：O439[机械工程—光学工程]