Query-by-Humming (QBH) systems base their operation
on aligning the melody sung/hummed by a user with a set
of candidate melodies retrieved from polyphonic songs.
While MIDI-based QBH builds on the premise of existing
annotated transcriptions for any candidate song, audiobased
research makes use of melody estimation algorithms
for the songs. In both cases, a melody abstraction process
is required for solving issues commonly found in queries
such as key transpositions or tempo deviations. ...
Query-by-Humming (QBH) systems base their operation
on aligning the melody sung/hummed by a user with a set
of candidate melodies retrieved from polyphonic songs.
While MIDI-based QBH builds on the premise of existing
annotated transcriptions for any candidate song, audiobased
research makes use of melody estimation algorithms
for the songs. In both cases, a melody abstraction process
is required for solving issues commonly found in queries
such as key transpositions or tempo deviations. Full automatic
music processes are commonly used for this, but
due to the reported limitations in state-of-the-art methods
for real-world queries, other possibilities should be considered.
In this work we explore three different melody representations,
ranging from a general time-series one to more
musical abstractions, which avoid full automatic transcription,
in the context of an audio-based QBH system. Results
show that this abstraction process plays a key role in the
overall accuracy of the system, obtaining the best scores
when temporal segmentation is dynamically performed in
terms of pitch change events in the melodic contour.
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