Choosing optimal maskers for existing soundscapes to effect a desired perceptual change via soundscape augmentation is non-trivial due to extensive varieties of maskers and a dearth of benchmark datasets with which to compare and develop soundscape augmentation models. To address this problem, we make publicly available the ARAUS (Affective Responses to Augmented Urban Soundscapes) dataset, which comprises a five-fold cross-validation set and independent test set totaling 25,440 unique subjective perceptual responses to augmented soundscapes presented as audio-visual stimuli. Each augmented soundscape is made by digitally adding "maskers" (bird, water, wind, traffic, construction, or silence) to urban soundscape recordings at fixed soundscape-to-masker ratios. Responses were then collected by asking participants to rate how pleasant, annoying, eventful, uneventful, vibrant, monotonous, chaotic, calm, and appropriate each augmented soundscape was, in accordance with ISO 12913-2:2018. Participants also provided relevant demographic information and completed standard psychological questionnaires. We perform exploratory and statistical analysis of the responses obtained to verify internal consistency and agreement with known results in the literature. Finally, we demonstrate the benchmarking capability of the dataset by training and comparing four baseline models for urban soundscape pleasantness: a low-parameter regression model, a high-parameter convolutional neural network, and two attention-based networks in the literature.

翻译：为现有声景选择最优掩蔽声以实现期望的感知改变，这一声景增强任务因掩蔽声种类繁多且缺乏用于比较和开发声景增强模型的基准数据集而极具挑战性。为解决此问题，我们公开了ARAUS（增强城市声景情感响应）数据集，该数据集包含一个五折交叉验证集和一个独立测试集，总计25,440个针对以视听刺激形式呈现的增强声景的独特主观感知响应。每个增强声景均通过以固定的声景-掩蔽声比例将“掩蔽声”（鸟鸣、水流、风声、交通、施工或静音）数字叠加至城市声景录音而生成。随后，我们要求参与者依据ISO 12913-2:2018标准，对每个增强声景在愉悦、烦扰、活跃、沉闷、活力、单调、混乱、平静及适宜度九个维度进行评分，以此收集响应数据。参与者还提供了相关人口统计信息并完成了标准心理问卷。我们对获得的响应进行了探索性和统计分析，以验证内部一致性以及与文献中已知结论的吻合度。最后，我们通过训练和比较四种城市声景愉悦度基线模型，展示了该数据集的基准测试能力：一个低参数回归模型、一个高参数卷积神经网络，以及两种文献中基于注意力机制的神经网络。