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Low-order modelling of Head Related Transfer Functions based on spectral smoothing and Principal Component Analysis

机译:基于谱平滑和主成分分析的头部相关传递函数的低阶建模

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The article presents wavelet-based spectral smoothing and Principal Component Analysis as a pre-processing step for Head Related Transfer Function (HRTF) filter design. Spectral smoothing of HRTF by means of Stationary Wavelet Transform discards low-value peaks and notches in HRTF which can be treated as noise. The Principal Component Analysis applied to the HRTF data extracts most important frequency components responsible for sound source localization in azimuth and elevation. These methods used in combination are used to reduce the order of filters that are applied in 3D audio rendering system.
机译:本文介绍了基于小波的频谱平滑和主成分分析,作为磁头相关传递函数(HRTF)滤波器设计的预处理步骤。借助固定小波变换对HRTF进行频谱平滑处理会丢弃HRTF中的低值峰值和陷波,这些噪声可以视为噪声。应用于HRTF数据的主成分分析可提取最重要的频率成分,这些成分负责声源在方位角和仰角中的定位。这些组合使用的方法用于减少3D音频渲染系统中应用的滤镜的顺序。

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