Individual head-related impulse responses (HRIRs) have been recognized as a key to creating high-fidelity virtual auditory spaces. Thus, fast and comprehensive acquisition of individual HRIRs has been a subject of continued research. Traditional stop-and-go measurement at discrete angles is time consuming and additionally requires spatial interpolation that has been tackled by mapping discrete HRIR tables to a spatial Fourier format. Uniformly continuous-azimuth recording with a moving apparatus, on the other hand, reduces acquisition time, but is noise-limited due to a very short observation time per angle. In the interest of both fast acquisition and high accuracy, in this paper, we propose direct retrieval of a spatial Fourier format from continuous-azimuth recordings at multiple simultaneous discrete elevations. Specifically, we fit a generative continuous-azimuth model of the recorded signal, based on the spatial Fourier representation, to the continuous recordings of individuals by least-squares. In this approach, the model is meant to entirely capture the spatial variation of the HRIR in azimuth, while the duration of the recording then systematically controls the noise rejection. The proposed time-domain treatment is free of block artifacts, but is numerically demanding. We outline how to take the special structure of the involved covariance matrices into account. Experimental results with simulated data and real recordings demonstrate that the HRIR performance in terms of binaural cues and reproducibility benefits from the proposed algorithm. Our method is hence practical in terms of low measurement time and high performance, while benefiting from increased computational power of current computers.

Fast and comprehensive acquisition of head-related impulse responses (HRIRs) continues in the interest of rich application scenarios. Various HRIR resolutions have been presented based on discrete stop-and-go measurement, or comprehensive measurement equipment, or continuous-azimuth acquisition with moving apparatus. Some researchers have further mapped the recorded HRIR table to spatial Fourier format. In the interest of both fast acquisition and high accuracy, in this paper, we propose direct retrieval of the spatial Fourier format from time-domain recordings of the measurement signal. Specifically, we revert to the moving apparatus for HRIR measurement and express a generative model of the recorded signal based on a hypothetical spatial Fourier basis. In this approach, the spatial-Fourier model is meant to entirely capture the spatial variation of the HRIR. By least-squares inference we can then fit that model to the entire recording. Due to our time-domain treatment the model and the processing are entirely clean from block artifacts, while the huge dimension of the problem requires attention in the implementation. It turns out that the proposed algorithm significantly outperforms the previous NLMS-based continuous-azimuth acquisition of HRIR with moving apparatus.

This paper is devoted to high-fidelity implementation of HRTF-based binaural rendering with fast head and source rotations in virtual acoustic reality. With an intuitive physical standpoint, we argue that head rotations should be rendered by a convolution model anchored in the sound receive-time. Conversely, the rendering of moving sound sources should be anchored in the sound emission-time, using retarded HRTF. Distant sound sources with bulk delay on the impulse response are handled efficiently by substituting the delay by additional retardation. The proposed rendering engine can utilize fast changing filter coefficients from a high resolution HRTF and satisfies some acoustic features as Doppler shift and dislocation inherently, which have been treated separately in conventional approaches or are neglected at all. To benchmark our rendering engine, we compare these acoustic features of the rendered signal with corresponding physical expectations.