Feature Aggregation in Joint Sound Classification and Localization Neural Networks

Abstract

Current state-of-the-art sound source localization (SSL) deep learning networks lack feature aggregation within their architecture. Feature aggregation within neural network architectures enhances model performance by enabling the consolidation of information from different feature scales, thereby improving feature robustness and invariance. We adapt feature aggregation sub-architectures from computer vision neural networks onto a baseline neural network architecture for SSL, the Sound Event Localization and Detection network (SELDnet). The incorporated sub-architecture are: Path Aggregation Network (PANet); Weighted Bi-directional Feature Pyramid Network (BiFPN); and a novel Scale Encoding Network (SEN). These sub-architectures were evaluated using two metrics for signal classification and two metrics for direction-of-arrival regression. The results show that models incorporating feature aggregations outperformed the baseline SELDnet, in both sound signal classification and localization. Among the feature aggregators, PANet exhibited superior performance compared to other methods, which were otherwise comparable. The results provide evidence that feature aggregation sub-architectures enhance the performance of sound detection neural networks, particularly in direction-of-arrival regression.