  Formalized in ISO 12913, the "soundscape" approach is a paradigmatic shift
towards perception-based urban sound management, aiming to alleviate the
substantial socioeconomic costs of noise pollution to advance the United
Nations Sustainable Development Goals. Focusing on traffic-exposed outdoor
residential sites, we implemented an automatic masker selection system (AMSS)
utilizing natural sounds to mask (or augment) traffic soundscapes. We employed
a pre-trained AI model to automatically select the optimal masker and adjust
its playback level, adapting to changes over time in the ambient environment to
maximize "Pleasantness", a perceptual dimension of soundscape quality in ISO
12913. Our validation study involving ($N=68$) residents revealed a significant
14.6 % enhancement in "Pleasantness" after intervention, correlating with
increased restorativeness and positive affect. Perceptual enhancements at the
traffic-exposed site matched those at a quieter control site with 6 dB(A) lower
$L_\text{A,eq}$ and road traffic noise dominance, affirming the efficacy of
AMSS as a soundscape intervention, while streamlining the labour-intensive
assessment of "Pleasantness" with probabilistic AI prediction.
