Pre-Semantic Phase Classification of Non-Linguistic Acoustic Signals

Most contemporary approaches to acoustic analysis focus on extracting meaning, intent, or semantic labels from sound. While effective for speech recognition and classification tasks, such approaches are poorly suited to non-linguistic signals such as animal vocalizations or natural environmental sounds, where meaning attribution is often speculative and anthropocentric. This paper introduces a pre-semantic structural framework for analyzing acoustic signals without assigning meaning, intention, or symbolic interpretation. Instead of classifying what a sound represents, the framework classifies how a signal appears as a regime of activity, using phase-based structural descriptors such as activation, repetition, compression, rupture, and integration. The approach is explicitly non-interpretive, non-psychological, and non-cognitive. It does not model emotion, intention, communication, or intelligence. It operates solely at the level of observable signal structure and temporal coherence. The framework is applicable to animal vocalizations, environmental sounds, and other non-linguistic acoustic phenomena, and is intended primarily as a safety-oriented perceptual layer for systems that must react conservatively under uncertainty. No algorithms, datasets, thresholds, or implementation details are disclosed. The contribution is architectural and conceptual, establishing a domain-independent distinction between listening as structural detection and listening as semantic interpretation.