Associated dataset for "Effects of Additive Noise in Binaural Rendering of Spherical Microphone Array Signals"

The instrumental evaluation utilized the Real-Time Spherical Microphone Renderer (ReTiSAR) for binaural reproduction in Python. The employed code state at that time should be used to reproduce the rendering results in this data set exactly. The frozen code state for this data set is available at:
https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2021.TASLP

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

conda env create --file ReTiSAR_environment_freeze.yml

source activate ReTiSAR_TASLP_freeze

Matlab script "generate_norm_levels.m":

• The level contributions used in the publication are contained in "record_CLL_levels.sh", therefore this needs to be executed only in case other level distributions should be generated
• Generate the string for a ReTiSAR configuration (as used in "record_CLL_levels.sh") to emulate normally contributing EM32 self-noise based on Forum Acusticum publication data
• Generate the string for aReTiSAR configuration (as used in "record_CLL_levels.sh") to emulate normally contributing GL162 self-noise based on Gaussian normal distribution

Matlab script "prepare_MagLS_HRIRs.m":

• Apply Magnitude Least Squares pre-processing to HRIRs (as used in "record_CLL_levels.sh")

Shell script "record_CLL_levels.sh":

• Record the output ear signals of the rendering pipeline at multiple head orientations for all investigated configurations (according to Table 1)
• All captured signals are contained in the respective configuration directory, e.g. "rec_25ch_Fliege_sh4" to "rec_338ch_Gauss_sh12"

Matlab script "calculate_CLL_levels.m":

• Read the individual rendering pipeline output recordings for arbitrary configurations
• Visualize the raw captured signals per configuration
• Visualize the RMS signal level variations over all head orientations per configuration
• Visualize the Interaural Level Difference variations over all head orientations per configuration
• Visualize the Composite Loudness Level variations over all head orientations per configuration (like Figure 2)
• Gather the above determined RMS, ILD, CLL, etc. metrics in a Matlab dataset per configuration (will be utilized in "plot_gathered_CLL_levels.m")
• All generated plots and Matlab datasets are contained in the respective configuration directory, e.g. "rec_25ch_Fliege_sh4" to "rec_338ch_Gauss_sh12"

Matlab script "plot_gathered_CLL_levels.m":

• Visualize the resulting Composite Loudness Level gradient detections over all head orientations for arbitrary combinations of configurations (like Figure 3 to Figure 14)
• All generated plots are contained in the "CLL_results" directory