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

The conducted instrumental and perceptual evaluation utilize the Real-Time Spherical Microphone Renderer (ReTiSAR) for binaural reproduction in Python. However, the provided execution configurations (see below) are probably not exactly in accordance with the latest ReTiSAR code base. Hence, the at the 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.ICASSP 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 should obtain exactly the same Python setup as utilized in the instrumental and perceptual evaluation in the publication: conda env create --file ReTiSAR_environment_freeze.yml source activate ReTiSAR_ICASSP_freeze Directory "SNR": Tools for instrumental evaluation (Section 4) Shell script to capture input and output signals of rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all specified configurations Matlab script to analyse captured signal and generate system transfer plots (Figure 1 to Figure 3 and further configurations) Directory "Relative Output Levels": Tools for preparation of perceptual evaluation (Section 5) Shell script to capture rendered uniformly contributing noise signals for all specified configurations Matlab script to analyse and level align captured signals and generate plot result plot (Figure 4) Directory "Absolute Output Levels": Tools for specification of perceptual evaluation (Section 5) Shell script to capture reproduced uniformly contributing noise signals for all specified configurations Matlab script to analyse the calibrated captured signals yielding the average level in the ear signals of 58.2 dBSPL (Section 5.1) Files in base directory and directory "Study Results": Tools for perceptual evaluation / user study (Section 5) Matlab GUI to conduct perceptual user study (employ by executing "ICASSP_gui.m", respective ReTiSAR instances are started and remote controlled by the GUI, raw study results will be stored in "results" directory) Matlab script to "calculate_conclusion.m" to analyse the raw study results and generate individual and conclusive result plots (Figure 5, Figure 6 and more)

本次仪器评测与感知评测均采用Python环境下的实时球面麦克风渲染器（Real-Time Spherical Microphone Renderer, ReTiSAR）完成双耳声重放任务。
但本次提供的运行配置（详见下文）大概率未完全匹配最新版ReTiSAR代码库。因此，若需精准复现本数据集内的渲染结果，应使用本研究开展时所采用的代码版本。
本数据集对应的固化代码版本可通过以下链接获取：https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP
请下载该渲染管线并按照配置指引完成搭建！搭建Python环境时，请使用本文附带的Conda环境配置文件。通过此方式，你将获得与本研究中仪器评测及感知评测完全一致的Python运行环境：
conda env create --file ReTiSAR_environment_freeze.yml
source activate ReTiSAR_ICASSP_freeze

「SNR」目录：仪器评测工具集（对应第4章节）
- Shell脚本：用于捕获所有指定配置下渲染管线的输入与输出信号，提取声场（目标/预期分量）与自噪声（非目标分量）
- Matlab脚本：用于分析捕获得到的信号，并生成系统传递特性绘图（图1至图3及其他相关配置的结果图）

「Relative Output Levels」目录：感知评测前期准备工具集（对应第5章节）
- Shell脚本：用于捕获所有指定配置下的均匀贡献噪声重放信号
- Matlab脚本：用于对捕获信号进行分析与电平校准对齐，并生成绘图结果（图4）

「Absolute Output Levels」目录：感知评测参数配置工具集（对应第5章节）
- Shell脚本：用于捕获所有指定配置下的均匀贡献噪声重放信号
- Matlab脚本：用于分析经校准的捕获信号，得到耳内信号的平均声压级为58.2 dBSPL（对应第5.1小节）

根目录与「Study Results」目录：感知评测/用户研究工具集（对应第5章节）
- Matlab图形用户界面（GUI）：用于开展感知用户研究，执行`ICASSP_gui.m`即可启动，该GUI会启动并远程控制对应的ReTiSAR实例，原始研究结果将存储于`results`目录中
- Matlab脚本`calculate_conclusion.m`：用于分析原始研究结果，并生成个体与综合结果绘图（图5、图6及其他相关结果图）