A Frequency-Structured Dilated Conformer Architecture for Full-Kit Automatic Drum Transcription

Automatic Drum Transcription (ADT) remains challenging due to overlapping spectral content among drum instruments, dense transient activity, and the increased difficulty associated with fine-grained full-kit transcription. This study investigates whether combining explicit spectral structuring with temporally extended modeling improves ADT performance under a controlled evaluation protocol. A frequency-structured architecture is proposed that integrates a convolutional front end, a Frequency-Structured Soft-Gated Module (FSSM) for coarse frequency-aware processing, and a Dilated Conformer encoder for multi-scale temporal context modeling while preserving frame-level output alignment. Experiments are conducted on the ENST Drums dataset using both 3-class and consolidated 8-class transcription settings. Under a fixed ±50 ms event-level evaluation protocol, the proposed model achieved mean Micro-F1 scores of 0.920 ± 0.001 and 0.872 ± 0.002 in the 3-class and 8-class settings, respectively, outperforming a reproduced CRNN baseline trained and evaluated under identical preprocessing, training, and evaluation protocols. Ablation studies indicate that FSSM contributes the largest performance improvement among the evaluated architectural components, while class-wise analyses show improvements across all instrument categories in the 8-class setting. Zero-shot evaluation on external datasets further demonstrates that the proposed architecture maintains higher performance than the baseline under distribution shift, although substantial degradation remains relative to in-domain evaluation. These findings suggest that combining explicit spectral structuring and temporally extended modeling can improve full-kit drum transcription performance under the evaluated conditions while highlighting the continuing challenges of cross-dataset generalization.