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引用次数: 0
摘要
在空间听力的研究中，需要考虑如何正确建模特定位置对应的hrtf (head-related transfer function, hrtf)或HRIRs (head-related impulse responses, HRIRs)的声学特性。在我们的工作中，我们成功地在小波变换领域进行了自适应非线性逼近。结果表明，HRIRs自适应非线性近似模型是一种更有效的数据约简模型，比基于相对误差的传统PCA (Karhunen-Loeve transform)模型速度更快，平均提高5 dB
Hrirs' Adaptive Non-Linear Approximation Model Based on Wavelet Transformation
During the study of spatial hearing, it is requisite to consider how to properly model the acoustical characteristics of HRTFs (head-related transfer functions: HRTFs) or HRIRs (head-related impulse responses: HRIRs) corresponding to certain positions. In our work, we managed to carry through adaptive non-linear approximation in the field of wavelet transformation. The results show that, the HRIRs' adaptive nonlinear approximation model is a more effective data reduction model, faster and averagely 5 dB better than the traditional PCA (Karhunen-Loeve transform) model based on relative error
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