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Soundscape augmentation or "masking" introduces wanted sounds into theacoustic environment to improve acoustic comfort. Usually, the masker selectionand playback strategies are either arbitrary or based on simple rules (e.g. -3dBA), which may lead to sub-optimal increment or even reduction in acousticcomfort for dynamic acoustic environments. To reduce ambiguity in the selectionof maskers, an automatic masker selection system (AMSS) was recently developed.The AMSS uses a deep-learning model trained on a large-scale dataset ofsubjective responses to maximize the derived ISO pleasantness (ISO 12913-2).Hence, this study investigates the short-term in situ performance of the AMSSimplemented in a gazebo in an urban park. Firstly, the predicted ISOpleasantness from the AMSS is evaluated in comparison to the in situ subjectiveevaluation scores. Secondly, the effect of various masker selection schemes onthe perceived affective quality and appropriateness would be evaluated. Intotal, each participant evaluated 6 conditions: (1) ambient environment with nomaskers; (2) AMSS; (3) bird and (4) water masker from prior art; (5) randomselection from same pool of maskers used to train the AMSS; and (6) selectionof best-performing maskers based on the analysis of the dataset used to trainthe AMSS.

Preliminary investigation of the short-term in situ performance of an automatic masker selection system