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In recent years, research on music transcription has focused mainly onarchitecture design and instrument-specific data acquisition. With the lack ofavailability of diverse datasets, progress is often limited to solo-instrumenttasks such as piano transcription. Several works have explored multi-instrumenttranscription as a means to bolster the performance of models on low-resourcetasks, but these methods face the same data availability issues. We proposeTimbre-Trap, a novel framework which unifies music transcription and audioreconstruction by exploiting the strong separability between pitch and timbre.We train a single autoencoder to simultaneously estimate pitch salience andreconstruct complex spectral coefficients, selecting between either outputduring the decoding stage via a simple switch mechanism. In this way, the modellearns to produce coefficients corresponding to timbre-less audio, which can beinterpreted as pitch salience. We demonstrate that the framework leads toperformance comparable to state-of-the-art instrument-agnostic transcriptionmethods, while only requiring a small amount of annotated data.

Timbre-Trap: A Low-Resource Framework for Instrument-Agnostic Music Transcription