Metrical-accent Aware Vocal Onset Detection in Polyphonic Audio
Georgi Bogomilov Dzhambazov, André Holzapfel, Ajay Srinivasamurthy, Xavier Serra
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The goal of this study is the automatic detection of onsets of the singing voice in polyphonic audio recordings. Starting with a hypothesis that the knowledge of the current position in a metrical cycle (i.e. metrical accent) can improve the accuracy of vocal note onset detection, we propose a novel probabilistic model to jointly track beats and vocal note onsets. The proposed model extends a state of the art model f ...
or beat and meter tracking, in which a-priori probability of a note at a specific metrical accent interacts with the probability of observing a vocal note onset. We carry out an evaluation on a varied collection of multi-instrument datasets from two music traditions (English popular music and Turkish makam) with different types of metrical cycles and singing styles. Results confirm that the proposed model reasonably improves vocal note onset detection accuracy compared to a baseline model that does not take metrical position into account.
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The goal of this study is the automatic detection of onsets of the singing voice in polyphonic audio recordings.
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Research context
2
Citations
16
References
Tasks
Stress (linguistics), Polyphony, Computer science, Linguistics, Psychology, Signal Processing, Physical Sciences
Methods
None detected
Domains
Speech recognition
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