Probabilistic Approach to Automatic Music Transcription from Audio Signals

We discuss automatic music transcription from audio input to music score by integrating our probabilistic approaches to multipitch spectral analysis, rhythm recognition and tempo estimation. In spectral analysis, acoustic energies in spectrogram are clustered into acoustic objects (i.e., music notes) with our method called harmonic-temporal-structured clustering (HTC) utilizing EM algorithm over a structured Gaussian mixture with constraints of harmonic structure and temporal smoothness. After onset and offset timings are found from separated energies of music notes through note power envelope modeling to obtain the piano-roll representation, the rhythm and tempo are simultaneously recognized and estimated in terms of maximum posterior probability given a probabilistic note duration models with HMM (hidden Markov model) and probabilistic "rhythm vocabulary." Variable tempo is also modeled by a smooth analytic curve. Rhythm recognition and tempo estimation is alternately performed to iteratively maximize the joint posterior probability. Experimental results are also shown.