  The existing piano fingering labeling statistical models usually consider the
constraints among the fingers and the correlation between fingering and notes,
and rarely include the relationship among the notes directly. The limited
learned finger-transfer rules often cause that some parts of the fingering
cannot be playable in fact. And traditional models often adopt the original
notes, which cannot help to explore the mapping nature between the pitches and
fingering. Inspired from manual-ly annotation which acquire the fingering
knowledge directly from pitch-difference, we proposed a pitch-difference
sequence and fingering (PdF) matching model. And to get playable fingering,
be-sides learned finger-transfer rules, prior finger-transfer knowledge is
especially combined into the model. In order to characterize the playable
performance of the model, we also presented a new evaluation index named
incapable-performing fingering rate (IFR). Comprehensive experimental re-sults
show that compared with the existing state-of-the-art third-order hidden Markov
labeling model, the general and the highest matching rate of our model
increases by 3% and 1.6% respective-ly, and the fingering for all scores can be
playable.
