01409nas a2200169 4500008004100000245008000041210006900121260003100190520085100221653002201072653002001094653002401114653002401138100002101162700002001183856003601203 2013 eng d00aLocal Group Delay based Vibrato and Tremolo Suppression for Onset Detection0 aLocal Group Delay based Vibrato and Tremolo Suppression for Onse aCuritiba, BrazilcNovember3 a
We present SuperFlux - a new onset detection algorithm with vibrato suppression. It is an enhanced version of the universal spectral flux onset detection algorithm, and reduces the number of false positive detections considerably by tracking spectral trajectories with a maximum filter. Especially for music with heavy use of vibrato (e.g., sung operas or string performances), the number of false positive detections can be reduced by up to 60% without missing any additional events. Algorithm performance was evaluated and compared to state-of-the-art methods on the basis of three different datasets comprising mixed audio material (25,927 onsets), violin recordings (7,677 onsets) and operatic solo voice recordings (1,448 onsets). Due to its causal nature, the algorithm is applicable in both offline and online real-time scenarios.
10alocal group delay10aonset detection10atremolo suppression10avibrato suppression1 aBöck, Sebastian1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/10901434nas a2200157 4500008004100000245005900041210005900100260003300159520094500192653001901137653002001156653002401176100002101200700002001221856003501241 2013 eng d00aMaximum Filter Vibrato Suppression for Onset Detection0 aMaximum Filter Vibrato Suppression for Onset Detection aMaynooth, IrelandcSeptember3 a
We present SuperFlux - a new onset detection algorithm with vibrato suppression. It is an enhanced version of the universal spectral flux onset detection algorithm, and reduces the number of false positive detections considerably by tracking spectral trajectories with a maximum filter. Especially for music with heavy use of vibrato (e.g., sung operas or string performances), the number of false positive detections can be reduced by up to 60% without missing any additional events. Algorithm performance was evaluated and compared to state-of-the-art methods on the basis of three different datasets comprising mixed audio material (25,927 onsets), violin recordings (7,677 onsets) and operatic solo voice recordings (1,448 onsets). Due to its causal nature, the algorithm is applicable in both offline and online real-time scenarios.
10amaximum filter10aonset detection10avibrato suppression1 aBöck, Sebastian1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/88