A multivariate approach for onset detection using supervised classification
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Date
2016
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Abstract
In this paper we introduce a new onset detection approach which incorporates a
supervised classification model for estimating the tone onset probability in signal
frames. In contrast to the most classical strategies where only one detection
function can be applied for signal feature extraction, the classification model
can be fitted on a large feature set. This is meaningful since, depending on the
music characteristics, some detection functions can be more advantageous that
the others.
Although the idea of the considering of many detection functions is not new
in the literature, these functions are, so far, treated in a univariate way by, e.g.,
building of weighted sums. This probably lies on the difficulties of the direct
transfer of the classification ideas to the onset detection task. The goodness
measure of onset detection is namely based on the comparison of two time
vectors while by the classification such a measure is derived from the framewise
matches of predicted and true labels.
In this work we first construct { based on several resent publications { a
comprehensive univariate onset detection algorithm which depends on many free
settable parameters. Then, the new multivariate approach also depending on
many free parameters is introduced. The parameters of the both onset detection
strategies are optimized for online and offline cases by utilizing an appropriate
validation technique. The main funding is that the multivariate strategy outperforms
the univariate one significantly regarding the F-measure. Furthermore,
the multivariate approach seems to be especially beneficial in online case since
it requires only the halve of the future signal information comparing to the best
setting of the univariate onset detection.
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Keywords
online onset detection, supervised classification, model based optimization