The composition information of audio recordings is highly
valuable for many tasks such as automatic music description
and music discovery. Given a music collection, two
typical scenarios are retrieving the composition(s) performed
in an audio recording and retrieving the audio recording(
s), where a composition is performed. We present a
composition identification methodology for these two tasks,
which makes use of music scores. Our methodology first
attempts to align a fragment of the music ...
The composition information of audio recordings is highly
valuable for many tasks such as automatic music description
and music discovery. Given a music collection, two
typical scenarios are retrieving the composition(s) performed
in an audio recording and retrieving the audio recording(
s), where a composition is performed. We present a
composition identification methodology for these two tasks,
which makes use of music scores. Our methodology first
attempts to align a fragment of the music score of a composition
with an audio recording. Next, it computes a similarity
from the best obtained alignment. True audio-score
pair emits a high similarity value. We repeat this procedure
between all audio recordings and music scores, and
filter the true pairs by a simple approach using logistic regression.
The methodology is specialized according to the
cultural-specific aspects of Ottoman-Turkish makam music
(OTMM), achieving 0.96 and 0.95 mean average precision
(MAP) for composition retrieval and performance retrieval
tasks, respectively. We hope that our method would
be useful in creating semantically linked music corpora for
cultural heritage and preservation, semantic web applications
and musicological studies.
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