Soundscape augmentation or "masking" introduces wanted sounds into the acoustic environment to improve acoustic comfort. Usually, the masker selection and playback strategies are either arbitrary or based on simple rules (e.g. -3 dBA), which may lead to sub-optimal increment or even reduction in acoustic comfort for dynamic acoustic environments. To reduce ambiguity in the selection of maskers, an automatic masker selection system (AMSS) was recently developed. The AMSS uses a deep-learning model trained on a large-scale dataset of subjective responses to maximize the derived ISO pleasantness (ISO 12913-2). Hence, this study investigates the short-term in situ performance of the AMSS implemented in a gazebo in an urban park. Firstly, the predicted ISO pleasantness from the AMSS is evaluated in comparison to the in situ subjective evaluation scores. Secondly, the effect of various masker selection schemes on the perceived affective quality and appropriateness would be evaluated. In total, each participant evaluated 6 conditions: (1) ambient environment with no maskers; (2) AMSS; (3) bird and (4) water masker from prior art; (5) random selection from same pool of maskers used to train the AMSS; and (6) selection of best-performing maskers based on the analysis of the dataset used to train the AMSS.

翻译：声景增强或“掩蔽”通过引入所需声音来改善声学环境中的声学舒适度。通常，掩蔽器选择与回放策略要么随意设定，要么基于简单规则（如-3 dBA），这可能导致动态声学环境中声学舒适度的次优提升甚至降低。为减少掩蔽器选择中的歧义，近期开发了一种自动掩蔽器选择系统（AMSS）。AMSS使用基于大规模主观响应数据集训练的深度学习模型，以最大化导出的ISO愉悦度（ISO 12913-2）。因此，本研究调查了部署于城市公园凉亭中的AMSS的短期现场性能。首先，评估AMSS预测的ISO愉悦度与现场主观评价得分的对比。其次，评估不同掩蔽器选择方案对感知情感质量和适宜性的影响。每位参与者共评估6种条件：（1）无掩蔽器的环境声；（2）AMSS；（3）现有技术中的鸟鸣掩蔽器；（4）水流掩蔽器；（5）从训练AMSS所用同一掩蔽器池中随机选择；（6）基于训练AMSS所用数据集分析选择性能最优掩蔽器。