Binaural rendering algorithms must balance simplicity and accuracy, due to the high degree of spectral complexity contained in a head-related transfer function (HRTF) dataset. A novel binaural rendering algorithm, termed principal component-base amplitude panning (PCBAP), has been developed which provides both accurate and efficient binaural rendering. The algorithm is based upon a time-domain principal component analysis of the HRTF, where resulting principal components serve as binaural rendering filters, and PC weights serve as panning gains. PCBAP is better suited to accurately reproducing both total level (TL) and interaural level difference (ILD) cues. When compared to time-aligned HRTFs fit to spherical harmonic functions and the magnitude least squares (MagLS) algorithms, PCBAP showed better performance for both cues at all reconstruction orders. Specifically, PCBAP can provide similar accuracy with 16-36 filters that other methods can only achieve with > 650 filters: a 95-98% reduction in computational requirements. Across frequency, PCBAP performs worse at lower orders below 3 kHz, but performance is superior at and above third order processing. PCBAP is also well-suited to accurately rendering ILD cues above 5 kHz in lateral directions. Other algorithms cannot render these fine details without using high processing orders ( ?? ? 9 ).
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