  This paper presents a polyphonic pitch tracking system able to extract both
framewise and note-based estimates from audio. The system uses several
artificial neural networks in a deep layered learning setup. First, cascading
networks are applied to a spectrogram for framewise fundamental frequency (f0)
estimation. A sparse receptive field is learned by the first network and then
used as a filter kernel for parameter sharing throughout the system. The f0
activations are connected across time to extract pitch contours. These contours
define a framework within which subsequent networks perform onset and offset
detection, operating across both time and smaller pitch fluctuations at the
same time. As input, the networks use, e.g., variations of latent
representations from the f0 estimation network. Finally, incorrect tentative
notes are removed one by one in an iterative procedure that allows a network to
classify notes within an accurate context. The system was evaluated on four
public test sets: MAPS, Bach10, TRIOS, and the MIREX Woodwind quintet, and
performed state-of-the-art results for all four datasets. It performs well
across all subtasks: f0, pitched onset, and pitched offset tracking.
