Associated dataset for "Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"

The conducted instrumental evaluation utilizes the Real-Time Spherical Microphone Renderer (ReTiSAR) for binaural reproduction in Python. The at that time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:
https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA

Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way, you will obtain exactly the same Python setup as utilized in the instrumental evaluation in the publication:

conda env create --file ReTiSAR_environment_freeze.yml

source activate ReTiSAR_FA_freeze

Directory "SMA sampling grids":

Visualization of spatial arrangement (like Figure 4) for all investigated spherical microphone array rendering configurations (Table 1)

Shell script "record_snr.sh":

Record the input and output signals of the rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all configurations at multiple head orientations

All captured signals are contained in the "SNR" directory

Matlab script "calculate_snr.m":

Visualize the raw captured input and output signals (like Figure 1 for all configurations)

Visualize the resulting signal-to-noise ratio (like Figure 2 for all configurations)

Visualize the comparison of the resulting signal-to-noise ratio of all configurations (Figure 3, also for the resulting SNR from signals with A-weighting)

All generated plots are contained in the "SNR" directory

Shell script "record_noise.sh":

Record the calibration and noise signals of the mh acoustic Eigenmike 32 spherical microphone array in the anechoic chamber at Chalmers University of Technology (Appendix)

All captured signals are contained in the "EM32 measurements" directory

Pictures of the measurement setup are contained in the "Pictures" subdirectory

Matlab script "calculate_EM32_noise_levels.m":

Determine the resulting target signal sensitivity and equivalent input noise levels for the investigated pre-amplification gains (Table 2)

Visualize the statistical distribution of the individual raw and weighted SMA channels (like Figure 6 for all configurations)

Visualize the spatial distribution of the individual raw and weighted SMA channels for all configurations

Visualize the smoothed and averaged magnitude spectra of the individual raw and weighted SMA channels (like Figure 5  for all configurations)