Effective Preprocessing in Modeling Head-Related Impulse Responses Based on Principal Components Analysis

It was found in previous works in modeling head-related impulse responses (HRIRs) using principal components analysis (PCA), both in frequency and time domain, that different sets of measured HRIRs were used, which were obtained from measurements by various institutions involving different kinds of subjects of human being, anesthetized live cat and acoustic manikin. Groups of researchers also applied different number of basis functions resulted from PCA, i.e. 4 – 10 basis functions. Then, the performance of the models was tested using different parameters, i.e. spectral distortion score and mean square error (MSE). Since there were varied factors mentioned above, a fair comparison among these models is difficult to achieve. Using PCA, we modeled the original HRIRs, minimum-phase HRIRs, direct-pulse HRIRs, normalized HRIRs in the time domain. However, in frequency domain, the models of magnitude head-related transfer functions (HRTFs), log-magnitude HRTFs, standardized log-magnitude HRTFs were performed. We performed a comprehensive comparison of various types of preprocessing of the previous data types in modeling HRIRs based on PCA using ten basis functions, CIPIC HRTF Database, and MSE. Our results showed that models of magnitude HRTFs had overall smallest average MSE. The second best models were achieved from minimum-phase HRIRs.