  Autonomous soundscape augmentation systems typically use trained models to
pick optimal maskers to effect a desired perceptual change. While acoustic
information is paramount to such systems, contextual information, including
participant demographics and the visual environment, also influences acoustic
perception. Hence, we propose modular modifications to an existing
attention-based deep neural network, to allow early, mid-level, and late
feature fusion of participant-linked, visual, and acoustic features. Ablation
studies on module configurations and corresponding fusion methods using the
ARAUS dataset show that contextual features improve the model performance in a
statistically significant manner on the normalized ISO Pleasantness, to a mean
squared error of $0.1194\pm0.0012$ for the best-performing all-modality model,
against $0.1217\pm0.0009$ for the audio-only model. Soundscape augmentation
systems can thereby leverage multimodal inputs for improved performance. We
also investigate the impact of individual participant-linked factors using
trained models to illustrate improvements in model explainability.
