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HRTF and Neural Network based Prediction and Simulation Method for Indoor Sports Acoustic

机译:基于HRTF和神经网络的室内运动声学预测和仿真方法

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To obtain personally the hearing feeling on the virtual sound field in an interactive computer environment, this paper presents a HRTF and neural network based prediction and simulation method for indoor sports acoustic. The method obtains room impulse response of random position through frequency domain interpolation, realizes motion predicting mechanism utilizing the fore-and-aft relativity of the user's motion position, and represents the motion of object by hierarchical data structure of quad tree. The mutual combination of the gradually coming in-out with head related transfer function HRTF and with neural network was accordingly turned. Multi-layer feed-forward neural network as the means to access any HRTF data in the indoors sound-field based on dynamic mechanisms was used. Protection of moving sound sources and room acoustic simulation based on dynamic mechanisms were determined the simulation of still sound source. Simulation results show that the method provides more reliable, the smoothly transition of the sequence of sound signals and the method of indoors acoustic simulation based on dynamic mechanism is feasible.
机译:要在交互式计算机环境中获得虚拟声场的听觉感觉,本文提出了一种用于室内运动声学的HRTF和神经网络的预测方法。该方法通过频域插值获得随机位置的室内脉冲响应,实现了利用用户运动位置的前后相对性的运动预测机制,并且表示通过四边形的分层数据结构对象的运动。因此,逐渐出现了HEAD相关传递函数HRTF和具有神经网络的相互结合。使用多层前馈神经网络作为访问基于动态机制的室内声场中的任何HRTF数据的方法。基于动态机制的移动声源和房间声学仿真的保护是确定静态声源的模拟。仿真结果表明,该方法提供了更可靠的,声音信号顺序的平稳过渡和基于动态机制的室内声学仿真方法是可行的。

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