  Fundamental frequency (f0) estimation from polyphonic music includes the
tasks of multiple-f0, melody, vocal, and bass line estimation. Historically
these problems have been approached separately, and only recently, using
learning-based approaches. We present a multitask deep learning architecture
that jointly estimates outputs for various tasks including multiple-f0, melody,
vocal and bass line estimation, and is trained using a large,
semi-automatically annotated dataset. We show that the multitask model
outperforms its single-task counterparts, and explore the effect of various
design decisions in our approach, and show that it performs better or at least
competitively when compared against strong baseline methods.
