Authors: Bauer, Nadja
Friedrichs, Klaus
Weihs, Claus
Title: A multivariate approach for onset detection using supervised classification
Language (ISO): en
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.
Subject Headings: online onset detection
supervised classification
model based optimization
URI: http://hdl.handle.net/2003/35699
http://dx.doi.org/10.17877/DE290R-17729
Issue Date: 2016
Appears in Collections:Sonderforschungsbereich (SFB) 823

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