Shift-Invariant Model for Polyphonic Music Transcription

In this paper, we propose an ecient model for automatic transcription of polyphonic music. The model extends the shift-invariant probabilistic latent component analysis method and uses pre-extracted and pre-shifted note templates from multiple instruments. Thus, the pro- posed system can eciently transcribe polyphonic music, while taking into account tuning deviations and frequency modulations. Additional system improvements utilising massive parallel computations with GPUs result in a system performing much faster than real-time. Experimental results using several datasets show that the proposed system can success- fully transcribe polyphonic music, outperforming several state-of-the-art approaches, and is over 140 times faster compared to a standard shift- invariant transcription model.