  This study addresses the application of deep learning techniques in joint
sound signal classification and localization networks. Current state-of-the-art
sound source localization deep learning networks lack feature aggregation
within their architecture. Feature aggregation enhances model performance by
enabling the consolidation of information from different feature scales,
thereby improving feature robustness and invariance. This is particularly
important in SSL networks, which must differentiate direct and indirect
acoustic signals. To address this gap, we adapt feature aggregation techniques
from computer vision neural networks to signal detection neural networks.
Additionally, we propose the Scale Encoding Network (SEN) for feature
aggregation to encode features from various scales, compressing the network for
more computationally efficient aggregation. To evaluate the efficacy of feature
aggregation in SSL networks, we integrated the following computer vision
feature aggregation sub-architectures into a SSL control architecture: Path
Aggregation Network (PANet), Weighted Bi-directional Feature Pyramid Network
(BiFPN), and SEN. These sub-architectures were evaluated using two metrics for
signal classification and two metrics for direction-of-arrival regression.
PANet and BiFPN are established aggregators in computer vision models, while
the proposed SEN is a more compact aggregator. The results suggest that models
incorporating feature aggregations outperformed the control model, the Sound
Event Localization and Detection network (SELDnet), in both sound signal
classification and localization. The feature aggregation techniques enhance the
performance of sound detection neural networks, particularly in
direction-of-arrival regression.
