  Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data,
and have been widely used in two main tasks of Automatic Music Transcription
(AMT): note segmentation, i.e. identifying the played notes after a multi-pitch
estimation, and sequential post-processing, i.e. correcting note segmentation
using training data. In this paper, we employ the multi-pitch estimation method
called Probabilistic Latent Component Analysis (PLCA), and develop AMT systems
by integrating different HMM-based modules in this framework. For note
segmentation, we use two different twostate on/o? HMMs, including a
higher-order one for duration modeling. For sequential post-processing, we
focused on a musicological modeling of polyphonic harmonic transitions, using a
first- and second-order HMMs whose states are defined through candidate note
mixtures. These different PLCA plus HMM systems have been evaluated
comparatively on two different instrument repertoires, namely the piano (using
the MAPS database) and the marovany zither. Our results show that the use of
HMMs could bring noticeable improvements to transcription results, depending on
the instrument repertoire.
