Recently, there has been much interest in automatic pitch estimation and note tracking of polyphonic music. To date, however, most techniques produce a representation where pitch estimates are not associated with any particular instrument or voice. Therefore, the actual tracks for each instrument are not readily accessible. Access to individual tracks is needed for more complete music transcription and additionally will provide a window to the analysis of higher constructs such as counterpoint and instrument theme imitation during a composition. In this paper, we present a method for tracking the pitches (F0s) of individual instruments in polyphonic music. The system uses a pre-learned dictionary of spectral basis vectors for each note for a variety of musical instruments. The method then formulates the tracking of pitches of individual voices in a probabilistic manner by attempting to explain the input spectrum as the most likely combination of musical instruments and notes drawn from the dictionary. The method has been evaluated on a subset of the MIREX multiple-F0 estimation test dataset, showing promising results.