  Music auto-tagging is crucial for enhancing music discovery and
recommendation. Existing models in Music Information Retrieval (MIR) struggle
with real-world noise such as environmental and speech sounds in multimedia
content. This study proposes a method inspired by speech-related tasks to
enhance music auto-tagging performance in noisy settings. The approach
integrates Domain Adversarial Training (DAT) into the music domain, enabling
robust music representations that withstand noise. Unlike previous research,
this approach involves an additional pretraining phase for the domain
classifier, to avoid performance degradation in the subsequent phase. Adding
various synthesized noisy music data improves the model's generalization across
different noise levels. The proposed architecture demonstrates enhanced
performance in music auto-tagging by effectively utilizing unlabeled noisy
music data. Additional experiments with supplementary unlabeled data further
improves the model's performance, underscoring its robust generalization
capabilities and broad applicability.
