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The Inverse Drum Machine: Source Separation Through Joint Transcription and Analysis-by-Synthesis

We introduce the Inverse Drum Machine (IDM), a novel approach to drum source separation that combines analysis-by-synthesis with deep learning. Unlike recent supervised methods that rely on isolated stems, IDM requires only transcription annotations. It jointly optimizes automatic drum transcription and one-shot drum sample synthesis in an end-to-end framework. By convolving synthesized one-shot samples with estimated onsets-mimicking a drum machine-IDM reconstructs individual drum stems and trains a neural network to match the original mixture. Evaluations on the StemGMD dataset show that IDM achieves separation performance on par with state-of-the-art supervised methods, while substantially outperforming matrix decomposition baselines.

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