基于小波变换模极大值重构原理的HRTFs平滑逼近预处理
doi: 10.3724/SP.J.1146.2005.00915
A Smoothing Method of Head-Related Transfer Functions Based on Reconstruction from Wavelet Transform Modulus Maxima
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摘要: 空间听觉的研究以及虚拟听觉空间的实现中，与头相关传递函数(Head-Related Transfer Functions, HRTFs)或与头相关冲激响应(Head-Related Impulse Responses, HRIRs)的高效建模对于隐含在HRTFs中的特征模式的识别有着极其重要的作用。作为建模前的一个重要环节，该文通过对HRTFs的时域奇异性特征分析和全部测量空间方位上HRIRs分布特点的统计判断，采用具有平移不变特性的多孔小波变换和相应的模极大值重构原理提出了一种HRTFs非线性平滑逼近预处理的方法。仿真实验结果表明，在设置的阈值门限一致的情况下，该文方法较PCA(Principal Component Analysis)和基于小波变换Mallat算法的逼近处理的性能分别提高了8.3dB和2.4dB。Abstract: In the research of spatial hearing and implementation of virtual auditory space, it is important to accurately model the latent acoustical clues in HRTFs(Head-Related Transfer Functions) or HRIRs(Head-Related Impulse Responses) related to certain position of sound source. As an essential preprocessing step, this work introduced a new smoothing means based on trous algorithm with translation-invariant and reconstruction from modulus maxima, and managed to carry through adaptive non-linear approximation in the field of wavelet transformation. The simulation results show that, under the uniform threshold, the performance of the new way is averagely 8.3dB better than that of traditional PCA(Principal Component Analysis) method, and 2.4dB than that of wavelet method using Mallat algorithm.
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