Synergistic Feature Fusion for Latent Lyrical Classification: A Gated Deep Learning Architecture

TL;DR

This paper introduces a gated deep learning architecture called Synergistic Fusion Layer (SFL) that effectively combines semantic and structural features for lyrical classification, outperforming traditional methods in accuracy and calibration.

Contribution

The paper presents a novel SFL architecture that uses a gating mechanism to fuse deep semantic and structural features, demonstrating superior performance and calibration over baseline models.

Findings

SFL achieved 98.94% accuracy and F1 score.

SFL significantly reduced calibration error and log loss.

Gating mechanism outperforms feature concatenation.

Abstract

This study addresses the challenge of integrating complex, high-dimensional deep semantic features with simple, interpretable structural cues for lyrical content classification. We introduce a novel Synergistic Fusion Layer (SFL) architecture, a deep learning model utilizing a gated mechanism to modulate Sentence-BERT embeddings (Fdeep) using low-dimensional auxiliary features (Fstruct). The task, derived from clustering UMAP-reduced lyrical embeddings, is reframed as binary classification, distinguishing a dominant, homogeneous cluster (Class 0) from all other content (Class 1). The SFL model achieved an accuracy of 0.9894 and a Macro F1 score of 0.9894, outperforming a comprehensive Random Forest (RF) baseline that used feature concatenation (Accuracy = 0.9868). Crucially, the SFL model demonstrated vastly superior reliability and calibration, exhibiting a 93% reduction in Expected Calibration Error (ECE = 0.0035) and a 2.5x lower Log Loss (0.0304) compared to the RF baseline (ECE = 0.0500; Log Loss = 0.0772). This performance validates the architectural hypothesis that non-linear gating is superior to simple feature concatenation, establishing the SFL model as a robust and trustworthy system for complex multimodal lyrical analysis.