  Many methods for automatic music transcription involves a multi-pitch
estimation method that estimates an activity score for each pitch. A second
processing step, called note segmentation, has to be performed for each pitch
in order to identify the time intervals when the notes are played. In this
study, a pitch-wise two-state on/off firstorder Hidden Markov Model (HMM) is
developed for note segmentation. A complete parametrization of the HMM sigmoid
function is proposed, based on its original regression formulation, including a
parameter alpha of slope smoothing and beta? of thresholding contrast. A
comparative evaluation of different note segmentation strategies was performed,
differentiated according to whether they use a fixed threshold, called "Hard
Thresholding" (HT), or a HMM-based thresholding method, called "Soft
Thresholding" (ST). This evaluation was done following MIREX standards and
using the MAPS dataset. Also, different transcription scenarios and recording
natures were tested using three units of the Degradation toolbox. Results show
that note segmentation through a HMM soft thresholding with a data-based
optimization of the {alpha,beta} parameter couple significantly enhances
transcription performance.
