Supplementary material for "Psychoacoustic assessment of synthetic sounds for electric vehicles in a virtual reality experiment"

The growing adoption of electric vehicles, known for their quieter operation compared to internal combustion engine vehicles, raises concerns about their detectability, particularly for vulnerable road users. To address this, regulations mandate the inclusion of exterior sound signals for electric vehicles, specifying minimum sound pressure levels at low speeds. These synthetic exterior sounds are often used in noisy urban environments, creating the challenge of enhancing detectability without introducing excessive noise annoyance. This study investigates the design of synthetic exterior sound signals that balance high noticeability with low annoyance. An audiovisual experiment with 14 participants was conducted using 15 virtual reality scenarios featuring a passing car. The scenarios included various sound signals, such as pure, intermittent, and complex tones at different frequencies. Two baseline cases, a diesel engine and only tyre noise, were also tested. Participants rated sounds for annoyance, noticeability, and informativeness using 11-point ICBEN scales. The findings highlight how psychoacoustic sound quality metrics predict annoyance ratings better than conventional sound metrics, providing insight into optimising sound design for electric vehicles. By improving pedestrian safety while minimising noise pollution, this research supports the development of effective and user-friendly exterior sound standards for electric vehicles.<br>The supplementary material contains:* /data: Contains data collected during the experiment.* /data/Participant_response: This folder includes output files generated by the Unity environment. Each folder follows the format: Participant_{participant_number}_{YYYYMMDD}_{HHMMSS} and includes:* - The participant's movement data, such as head movement and hand movements.* - Responses to in-experiment questions.* - A mapping file indicating the sequence of trials per participant.* /data/Response_form/: Contains intake and post-trial questionnaire data and forms:* - intake-questionnaire.csv: Responses to the intake questionnaire.* - post-questionnaire.csv: Responses to the post-trial questionnaire.* - intake-questionnaire.pdf: PDF of the intake questionnaire form.* - post-questionnaire.pdf: PDF of the post-trial questionnaire form.* mapping.csv: Mapping of stimuli.* master_datasheet.mat: Responses of the listening experiment.* measurements.mat: List of the audio files to be analysed.* metrics.mat: Sound quality metrics that the code produces.* /sounds: Audio files used as stimuli in the Unity experimental setup (as emitted by the EV).* /sounds_raw: Raw audio files used for SQM analysis (as perceived at the observer position).* /SQAT-feature-EPNL: SQAT framework as downloaded from https://github.com/ggrecow/SQAT* sqm_analysis.m: Analysis to produce figures.* /unity_environment: Unity project containing the virtual environment used in the experiment.<br>NOTE:A public repository with the maintained Unity code is available at: https://github.com/Shaadalam9/sound-ev

相较于内燃机车辆，电动汽车以运行静谧性更佳的特点日益普及，但其可被探测性问题随之引发关注，这一问题对于弱势道路使用者而言尤为突出。为应对这一问题，相关法规强制要求电动汽车加装外部声音信号，并明确了低速行驶时的最低声压级要求。这类人工合成的外部声音信号在嘈杂的城市环境中常被使用，这就带来了新的挑战：需在提升可探测性的同时，避免产生过度的噪声烦扰。本研究旨在设计能够兼顾高可辨识度与低烦扰度的电动汽车外部声音信号。本研究招募14名受试者开展视听实验，实验设置了15种包含驶过车辆的虚拟现实场景。场景中涵盖了多种声音信号方案，包括不同频率的纯音、间歇音与复合音，同时设置了柴油发动机与仅轮胎噪声两种基线工况作为对照。受试者采用11点ICBEN量表（ICBEN）对声音的烦扰度、可辨识度与信息性进行评分。研究结果表明，心理声学音质指标相较于传统声学指标能更好地预测烦扰评分，这为电动汽车外部声音的优化设计提供了理论支撑。本研究在提升行人安全的同时最大限度降低噪声污染，可为电动汽车外部声音标准的开发与实施，助力制定高效且人性化的电动汽车外部声音标准。

补充材料包含：
* /data：存储实验过程中采集的数据。
* /data/Participant_response：该文件夹包含Unity环境生成的输出文件，每个子文件夹遵循命名格式：Participant_{参与者编号}_{YYYYMMDD}_{HHMMSS}，其中包含：
- 受试者的运动数据，如头部运动与手部运动数据。
- 实验内问题的作答结果。
- 用于标注每名受试者试验顺序的映射文件。
* /data/Response_form/：包含入组与试验后问卷数据及问卷表单：
- intake-questionnaire.csv：入组问卷的作答数据。
- post-questionnaire.csv：试验后问卷的作答数据。
- intake-questionnaire.pdf：入组问卷表单的PDF文件。
- post-questionnaire.pdf：试验后问卷表单的PDF文件。
* mapping.csv：刺激物映射表。
* master_datasheet.mat：听觉实验的作答数据。
* measurements.mat：待分析音频文件清单。
* metrics.mat：代码生成的音质指标数据。
* /sounds：Unity实验装置中用作刺激物的音频文件（即电动汽车发出的声音）。
* /sounds_raw：用于SQM分析的原始音频文件（即观察者位置处感知到的声音）。
* /SQAT-feature-EPNL：从https://github.com/ggrecow/SQAT 下载的SQAT框架。
* sqm_analysis.m：用于生成图表的分析脚本。
* /unity_environment：包含实验所用虚拟环境的Unity项目。

注：可在以下公开仓库获取该项目维护的Unity代码：https://github.com/Shaadalam9/sound-ev