Deciding exactly when to move audio objects in immersive mixes is a labor-intensive artistic task. Current tools react strictly to instantaneous frequency overlaps, lacking the macroscopic awareness required for musically intentional spatial transitions. To model these decisions, we propose SEND (Spatial Event Neural Detector). Its dual-stream architecture analyzes the target track against its background context, combining a Spec-TNT backbone and a Temporal Convolutional Network (TCN) to capture hierarchical spectral features and precise rhythmic cues. Their dynamic interplay is modeled via a novel Cross-Track Gating Interaction (CTGI) mechanism.