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Effective Preprocessing in Modeling Head-Related Impulse Responses Based on Principal Components Analysis
Hugeng , Dadang Gunawan, Wahidin Wahab
Pages - 201 - 212     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 4   Issue - 4    |    Publication Date - October 2010  Table of Contents
HRIR Model, HRTF Model, 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.
CITED BY (11)  
1 ugeng, H., Wahab, W., & Gunawan, D. (2015). A New Selection Method of Anthropometric Parameters in Individualizing HRIR. TELKOMNIKA (Telecommunication Computing Electronics and Control), 13(3).
2 Wahab, W., & Gunawan, D. (2015). A New Selection Method of Anthropometric Parameters in Individualizing Head-Related Impulse Responses. Telkomnika, 13(3).
3 Gallegos, E. A. T., Bustamante, F. O., & Cosío, F. A. Sistema de foto-antropometría automatizada y su uso en la personalización de funciones de transferencia acústica referidas a la cabeza (HRTF).
4 Rothbucher, M., Kuhn, A., & Diepold, K. HRTF Customization using the LDV HRTF-database.
5 Ramos, O. A., Tommasini, F. C., Araneda, M., Ferreyra, S. P., & Cravero, A. Mecánica Computacional, Volume XXXI. Number 26. Acoustics and Mechanical Vibrations (B).
6 Kuhn, A., & Rothbucher, M. (2013). HRTF Customization by Regression. Masterarbeit, Technische Universität München.
7 HRamos, O. A., Tommasini, F. C., Ferreyra, S. P., Cravero, A., & Guido, M. (2013).Mecánica Computacional, Volume XXXII. Number 35. Acoustics and Vibrations (B).
8 Ramos, O. A., Tommasini, F. C., Araneda, M., Ferreyra, S. P., & Cravero, A. (2012).Preprocesamiento eficaz para modelar las respuestas impulsivas de cabeza, utilizando análisis de componentes principales.
9 Andreopoulou, A., & Roginska, A. (2011, October). Towards the creation of a standardized HRTF repository. In Audio Engineering Society Convention 131. Audio Engineering Society.
10 Hugeng, H., Wahab, W., & Gunawan, D. (2011). The Effectiveness of Chosen Partial Anthropometric Measurements in Individualizing Head-Related Transfer Functions on Median Plane. Journal of ICT Research and Applications, 5(1), 35-56.
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Mr. Hugeng
Dept.Electrical Engineering - Indonesia
Professor Dadang Gunawan
University of Indonesia - Indonesia
Dr. Wahidin Wahab
University of Indonesia - Indonesia