This work deals with the automatic transcription and characterization
of flamenco guitar, with a focus on short melodic interludes
improvised between sung verses. These are called falsetas
in the flamenco argot and are very challenging for manual and automatic
transcription due to their fast and highly ornamented nature.
However, they are a key resource for guitar players to practice.
We adapted a state of the art singing transcription algorithm
to process an audio signal containing one or ...
This work deals with the automatic transcription and characterization
of flamenco guitar, with a focus on short melodic interludes
improvised between sung verses. These are called falsetas
in the flamenco argot and are very challenging for manual and automatic
transcription due to their fast and highly ornamented nature.
However, they are a key resource for guitar players to practice.
We adapted a state of the art singing transcription algorithm
to process an audio signal containing one or several guitar falsetas
and extract their symbolic representation. The algorithms first
perform a segmentation to locate the guitar fragments and then
a symbolic transcription of these segments into symbolic representation.
In order to evaluate it, we collected the first (to our
knowledge) annotated falseta datasets. Our results confirm the
difficulty of the task, and a detailed study of two transcriptions revealed
that combining the algorithm with specific musical knowledge
about the scale used by the song, improves the performance
of the system. Our approach follows the principles of research reproducibility,
and the system is integrated in a computer-assisted
paradigm, where the user complements the automatic annotation
with a priory knowledge to generate a final transcription.
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