Wavelet-based Spectral Smoothing for Head-Related Transfer Function Filter Design
Three wavelet-based spectral smoothing techniques are presented in this paper as a pre-processing stage for head-related transfer function (HRTF) filter design. These wavelet-based methods include wavelet denoising, wavelet approximation, and redundant wavelet transform. These methods are used with time-domain parametric filter design methods to reduce the order of IIR filters which is useful for real-time implementation of immersive audio systems. Results of a subjective listening test are then presented in order to justify the perceptual validity of the investigated smoothing methods. Results show that wavelet based spectral smoothing methods are beneficial in reducing the filter order and increasing the perception of localization without introducing any noticeable effect on timbre.
Click to purchase paper or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members, $5 for AES members and is free for E-Library subscribers.