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

机译:基于光谱平滑和主成分分析的头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光谱平滑和主成分分析作为头部相关传递函数(HRTF)滤波器设计的预处理步骤。通过静止小波变换的HRTF的光谱平滑丢弃HRTF中的低值峰和槽口,其可以被视为噪声。应用于HRTF数据的主要成分分析提取负责方位角和高程中声源定位的最重要的频率分量。这些用于组合使用的方法用于减少应用于3D音频渲染系统中的滤波器的顺序。

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