An Efficient Shift-Invariant Model for Polyphonic Music Transcription

In this paper, we propose an efficient 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 proposed system can efficiently 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 successfully transcribe polyphonic music, outperforming several state-of-the-art approaches, and is over 140 times faster compared to a standard shiftinvariant transcription model.