面向虚拟现实场景的房间脉冲响应计算模型  

Room impulse response calculation model for virtual reality scenarios

作　　者：李知禹 王晶[1] 岳欣雯 杨立东[2] 赵胜辉[1] 谢湘[1] LI Zhiyu;WANG Jing;YUE Xinwen;YANG Lidong;ZHAO Shenghui;XIE Xiang(School of Information and Electronics,Beijing Institute of Technology Beijing 100081;School of Digital and Intelligent Industry,Inner Mongolia University of Science&Technology Baotou 014010)

机构地区：[1]北京理工大学信息与电子学院,北京100081 [2]内蒙古科技大学数智产业学院,包头014010

出　　处：《声学学报》2024年第6期1186-1196,共11页Acta Acustica

基　　金：国家自然科学基金项目(62071039);内蒙古自治区科技计划项目(2023YFSW0006)资助。

摘　　要：提出了一种面向虚拟现实应用场景的房间脉冲响应计算模型,将深度学习神经网络技术与心理声学感知参数结合,能够高效地从虚拟现实场景信息中预测具有听觉感知意义的房间脉冲响应。在确保高质量预测结果的同时,该模型可以满足虚拟现实音频场景中生成房间脉冲响应所需的实时性、高采样率、非受限长度和轻量化的要求。模型首先通过图卷积神经网络将场景中的声学信息编码,然后通过神经声场与转置卷积模型将声学信息解码得到房间脉冲响应感知参数,最后根据房间脉冲响应感知参数恢复出房间脉冲响应信号。实验结果表明,所提模型在房间脉冲响应生成质量、计算开销以及功能性方面都有较大的优势,可较好地满足虚拟现实音频对于实时生成房间脉冲响应的需求。This study proposes a room impulse response(RIR)computation model tailored for virtual reality applications,integrating deep learning neural network techniques with psychoacoustic perception parameters.This model can efficiently predict perceptually meaningful RIRs from virtual reality scene data while ensuring high-quality predictions.It meets the requirements for real-time generation,high sampling rate,unrestricted length,and lightweight implementation in virtual reality audio scenarios.The model first encodes the acoustic information from the scene using a graph convolutional neural network,then decodes this information through a neural sound field and transposed convolution model to obtain the RIR perception parameters.Finally,the RIR signal is reconstructed from these parameters.Experimental results demonstrate that the proposed model offers significant advantages in RIR generation quality,computational efficiency,and functionality,making it well-suited to meet the real-time RIR generation needs of virtual reality audio.

关 键 词：房间脉冲响应 深度学习 感知参数 虚拟现实 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]