Kalman filtering application in automatic music transcription

In this paper we discuss the problem of partial tracking as applied to music signals, and propose a tracking algorithm based on Kalman filtering. This algorithm is capable of tracking both frequency and power partials, which are used in different areas of music signal analysis. We introduce a set of state-space models for our signals based on the evolution of frequency and amplitude in different classes of musical instruments. These prior models are used to estimate future values of partial tracks in successive time frames of our spectral data. We present and evaluate the performance of our tracker in different possible scenarios where there are crossing partials or vibrato