Drum beats are often conceptualized and produced as arrangements of repeated single-drum sound events or one-shots. Drum Analysis-by-Synthesis (AbS) models combine this structural assumption with reconstruction-based training to perform MIR tasks such as drum transcription, drum source separation, and one-shot extraction, offering a flexible alternative to more traditional task-specific supervision. However, leading AbS approaches struggle to generalize to a broad range of drum timbres (e.g., electronic drum kits), rely on high-quality paired audio-transcription training data, and do not model intra-class timbral variation within a single arrangement (e.g., multiple kick sounds). To overcome these limitations, we propose D-Composer, an autoregressive language modeling framework that decomposes drum recordings into labeled sound events and their onset times. In drum transcription and one-shot extraction tasks, D-Composer performs comparably to or better than a state-of-the-art AbS system and task-specific baselines while generalizing to a broader range of drum timbres. Our results demonstrate that when equipped with abundant synthetic data and compact acoustic token representations, language modeling provides a simple recipe for unifying transcription and sound event decomposition.

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