The work focuses on background music in short videos, including clips lasting 15 to 60 seconds and recorded in uncontrolled environments. Such audio may be truncated, degraded or unavailable in the form of lyrics, including when a track is instrumental. The study notes that emotion-related metadata for these clips is still produced mainly through manual annotation or generic audio taggers that were not designed for short-video recordings. SALF-Net was designed to address these constraints by combining acoustic information with lyric information when available while remaining functional when one modality is missing.

The audio branch adapts a music-domain self-supervised transformer encoder to clip-level inputs through masked segment sampling and statistics pooling. A separate lyric branch uses a pretrained language model to encode lyrics obtained through automatic transcription or metadata. Between them, a gated cross-modal fusion module combines the two representations. Modality dropout is used during training so that the system can continue operating when lyrics are absent or unreliable, a condition described as common for instrumental tracks and noisy recordings. SALF-Net also has a multi-task objective that jointly predicts multi-label emotion tags and valence-arousal values, using the dimensional task to regularize the geometry of the learned tag representation.

Evaluation combined the mood-and-theme subset of the MTG-Jamendo dataset, the DEAM dimensional emotion benchmark and the newly assembled short-video test collection of 30,000 clips. The reported results indicate that SALF-Net performed better than strong self-supervised baselines under the tested conditions. The student model’s latency result suggests that much of the teacher model’s performance can be retained in a form suited to faster deployment. The authors interpret the findings as evidence that accurate, robust and deployable emotion tagging is feasible at short-video scale without relying on platform-private annotation budgets. The supplied study abstract does not provide genre-specific results, platform-wide testing, human-judgment comparisons or performance across every uncontrolled recording environment, so those aspects remain outside the reported evidence.

Subject of Research: Self-supervised audio-lyric fusion for emotion tagging of short-video music

Article Title: Automatic music emotion tag generation for short-video platforms: a self-supervised audio-lyric fusion network with valence-arousal regularization

Article References: Liu, Z., & Hong, H. (2026). Automatic music emotion tag generation for short-video platforms: a self-supervised audio-lyric fusion network with valence-arousal regularization. Scientific Reports. https://doi.org/10.1038/s41598-026-76372-5

Image Credits: AI Generated

DOI: 10.1038/s41598-026-76372-5

Keywords: music emotion recognition, short-video metadata, self-supervised learning, audio-lyric fusion, valence-arousal regression, multimodal learning, modality dropout, model distillation, emotion tags, short-video audio, Automatic, music