Musical notes are often modeled as a discrete sequence
of points on a frequency spectrum with possibly different
interval sizes such as just-intonation. Computational
descriptions abstracting the pitch content in audio music
recordings have used this model, with reasonable success
in several information retrieval tasks. In this paper, we
argue that this model restricts a deeper understanding of
the pitch content. First, we discuss a statistical model of
musical notes which widens the scope ...
Musical notes are often modeled as a discrete sequence
of points on a frequency spectrum with possibly different
interval sizes such as just-intonation. Computational
descriptions abstracting the pitch content in audio music
recordings have used this model, with reasonable success
in several information retrieval tasks. In this paper, we
argue that this model restricts a deeper understanding of
the pitch content. First, we discuss a statistical model of
musical notes which widens the scope of the current one
and opens up possibilities to create new ways to describe
the pitch content. Then we present a computational approach
that partially aligns the audio recording with its
music score in a hierarchical manner first at metrical cyclelevel
and then at note-level, to describe the pitch content
using this model. It is evaluated extrinsically in a classification
test using a public dataset and the result is shown
to be significantly better compared to a state-of-the-art approach.
Further, similar results obtained on a more challenging
dataset which we have put together, reinforces that
our approach outperforms the other.
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