We present a new neural network based peak-picking algorithm for common onset detection functions. Compared to existing hand-crafted methods it yields a better performance and leads to a much lower number of false negative detections. The performance is evaluated on basis of a huge dataset with over 25k annotated onsets and shows a significant improvement over existing methods in cases of signals with previously unknown levels.
JF - Proceedings of the 6th International Workshop on Machine Learning and Music CY - Prague, Czech Republic ER - TY - CONF T1 - Local Group Delay based Vibrato and Tremolo Suppression for Onset Detection T2 - Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) Y1 - 2013 A1 - Sebastian Böck A1 - Widmer, Gerhard KW - local group delay KW - onset detection KW - tremolo suppression KW - vibrato suppression AB -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.
JF - Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) CY - Curitiba, Brazil ER - TY - CONF T1 - Maximum Filter Vibrato Suppression for Onset Detection T2 - Proceedings of the 16th International Conference on Digital Audio Effects (DAFx-13) Y1 - 2013 A1 - Sebastian Böck A1 - Widmer, Gerhard KW - maximum filter KW - onset detection KW - vibrato suppression AB -
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.
JF - Proceedings of the 16th International Conference on Digital Audio Effects (DAFx-13) CY - Maynooth, Ireland ER - TY - CONF T1 - Musical Onset Detection with Convolutional Neural Networks T2 - Proceedings of the 6th International Workshop on Machine Learning and Music Y1 - 2013 A1 - Schlüter, Jan A1 - Sebastian Böck KW - convolutional neural networks KW - onset detection JF - Proceedings of the 6th International Workshop on Machine Learning and Music CY - Prague, Czech Republic ER - TY - Generic T1 - Rhytmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio T2 - Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) Y1 - 2013 A1 - Krebs, Florian A1 - Sebastian Böck A1 - Widmer, Gerhard AB -Rhythmic patterns are an important structural element in music. This paper investigates the use of rhythmic pattern modeling to infer metrical structure in musical audio recordings. We present a Hidden Markov Model (HMM) based system that simultaneously extracts beats, downbeats, tempo, meter, and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. al, which we further modified by introducing a new observation model: rhythmic patterns are learned directly from data, which makes the model adaptable to the rhythmical structure of any kind of music. For learning rhythmic patterns and evaluating beat and downbeat tracking, 697 ballroom dance pieces were annotated with beat and measure information. The results showed that explicitly modeling rhythmic patterns of dance styles drastically reduces octave errors (detection of half or double tempo) and substantially improves downbeat tracking.
JF - Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) ER -