Evaluating the Online Capabilities of Onset Detection Methods.
Sprache des Vortragstitels:
Englisch
Original Kurzfassung:
In this paper, we evaluate various onset detection algorithms
in terms of their online capabilities. Most methods
use some kind of normalization over time, which renders
them unusable for online tasks. We modified existing
methods to enable online application and evaluated their
performance on a large dataset consisting of 27,774 annotated
onsets. We focus particularly on the incorporated
preprocessing and peak detection methods. We show that,
with the right choice of parameters, the maximum achievable
performance is in the same range as that of offline
algorithms, and that preprocessing can improve the results
considerably. Furthermore, we propose a new onset detection
method based on the common spectral flux and a new
peak-picking method which outperforms traditional methods
both online and offline and works with audio signals
of various volume levels.
Forschungs-