  Real music signals are highly variable, yet they have strong statistical
structure. Prior information about the underlying physical mechanisms by which
sounds are generated and rules by which complex sound structure is constructed
(notes, chords, a complete musical score), can be naturally unified using
Bayesian modelling techniques. Typically algorithms for Automatic Music
Transcription independently carry out individual tasks such as multiple-F0
detection and beat tracking. The challenge remains to perform joint estimation
of all parameters. We present a Bayesian approach for modelling music audio,
and content analysis. The proposed methodology based on Gaussian processes
seeks joint estimation of multiple music concepts by incorporating into the
kernel prior information about non-stationary behaviour, dynamics, and rich
spectral content present in the modelled music signal. We illustrate the
benefits of this approach via two tasks: pitch estimation, and inferring
missing segments in a polyphonic audio recording.
