Unsupervised Transcription of Piano Music

We demonstrate a polyphonic transcription system capable of recognizing live music from an acoustic piano and representing it in symbolic form. In addition, we show how the system can be used to resynthesize performances by famous pianists using new instruments. Our transcription system uses a new probabilistic model that reflects the process by which discrete musical events give rise to acoustic signals that are then superimposed to produce the observed data. As a result, the inference procedure for our model naturally resolves the source separation problem introduced by the the piano’s polyphony. In order to adapt to the properties of a new instrument or acoustic environment being transcribed, we learn recording-specific spectral profiles and temporal envelopes in an unsupervised fashion.