  Soundscape studies typically attempt to capture the perception and
understanding of sonic environments by surveying users. However, for long-term
monitoring or assessing interventions, sound-signal-based approaches are
required. To this end, most previous research focused on psycho-acoustic
quantities or automatic sound recognition. Few attempts were made to include
appraisal (e.g., in circumplex frameworks). This paper proposes an artificial
intelligence (AI)-based dual-branch convolutional neural network with
cross-attention-based fusion (DCNN-CaF) to analyze automatic soundscape
characterization, including sound recognition and appraisal. Using the DeLTA
dataset containing human-annotated sound source labels and perceived annoyance,
the DCNN-CaF is proposed to perform sound source classification (SSC) and
human-perceived annoyance rating prediction (ARP). Experimental findings
indicate that (1) the proposed DCNN-CaF using loudness and Mel features
outperforms the DCNN-CaF using only one of them. (2) The proposed DCNN-CaF with
cross-attention fusion outperforms other typical AI-based models and
soundscape-related traditional machine learning methods on the SSC and ARP
tasks. (3) Correlation analysis reveals that the relationship between sound
sources and annoyance is similar for humans and the proposed AI-based DCNN-CaF
model. (4) Generalization tests show that the proposed model's ARP in the
presence of model-unknown sound sources is consistent with expert expectations
and can explain previous findings from the literature on sound-scape
augmentation.
