| Title |
Auto-detection of Spectral Notches of Head-related Transfer Function using Wavelet Multi-resolution Analysis |
| Authors |
(Bahaa Al-Sheikh) ; (Mohammad Shukri Salman) ; (Alaa Eleyan) |
| DOI |
https://doi.org/10.5573/IEIESPC.2021.10.5.375 |
| Keywords |
HRTF; Spectral notches; 3D auditory display; Wavelet; Multi-resolution analysis; Auto-detection |
| Abstract |
The spectral features of pressure characterized by the human outer ear, head, and torso are vital for sound localization in humans, especially for sound above 5 kHz. Many studies have shown that spectral notches contribute significantly as cues for this purpose. These spectral notches and their center frequencies depend on the shape and geometry of each individual, so their characteristics are different between individuals. We used wavelet multi-resolution analysis to automatically detect the center frequencies of spectral notches in head-related transfer functions (HRTFs). Auto-detection of these notches at certain locations for an individual helps in using suitable, complete, 3D HRTFs from HRTF datasets that have similar notch characteristics for an individual. Symlet and Daubechies wavelets were successfully used at different decomposition levels for this purpose. Symlet2 gives the best performance in terms of the auto-detection sensitivity. |