Multiple-instrument Polyphonic Music Transcription Using a Convolutive Probabilistic Model

In this paper, a method for automatic transcription of music signals using a convolutive probabilistic model is proposed, by extending the shift-invariant Probabilistic Latent Component Analysis method. Several note templates from multiple orchestral instruments are extracted from monophonic recordings and are used for training the transcription system. By incorporating shift-invariance into the model along with the constant-Q transform as a timefrequency representation, tuning changes and frequency modulations such as vibrato can be better supported. For postprocessing, Hidden Markov Models trained on MIDI data are employed, in order to favour temporal continuity. The system was tested on classical and jazz recordings from the RWC database, on recordings from a Disklavier piano, and a woodwind quintet recording. The proposed method, which can also be used for pitch content visualization, outperforms several state-of-the-art approaches for transcription, using a variety of error metrics.