@conference {130, title = {Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio}, booktitle = {53rd AES Conference on Semantic Audio}, year = {2014}, month = {01/2014}, address = {London, UK}, author = {Collins, Tom and Sebastian B{\"o}ck and Krebs, Florian and Widmer, Gerhard} } @conference {131, title = {The Complete Classical Music Companion V0.9}, booktitle = {53rd AES Conference on Semantic Audio}, year = {2014}, month = {01/2014}, address = {London, UK}, author = {Andreas Arzt and Sebastian B{\"o}ck and Flossmann, Sebastian and Frostel, Harald and Gasser, Martin and Widmer, Gerhard} } @proceedings {141, title = {Improved musical onset detection with convolutional neural networks}, journal = {Proceedings of the 39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)}, year = {2014}, month = {May}, author = {Jan Schl{\"u}ter and Sebastian B{\"o}ck} } @conference {270, title = {The Piano Music Companion}, booktitle = {Proceedings of the Conference on Prestigious Applications of Intelligent Systems (PAIS)}, year = {2014}, author = {Andreas Arzt and Sebastian B{\"o}ck and Flossmann, S. and Frostel, H. and Gasser, M. and Cynthia C. S. Liem and Widmer, G.} } @conference {110, title = {Enhanced peak picking for onset detection with recurrent neural networks}, booktitle = {Proceedings of the 6th International Workshop on Machine Learning and Music}, year = {2013}, month = {September}, address = {Prague, Czech Republic}, abstract = {

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.

}, keywords = {onset detection, peak-picking}, author = {Sebastian B{\"o}ck and Schl{\"u}ter, Jan and Widmer, Gerhard} } @conference {109, title = {Local Group Delay based Vibrato and Tremolo Suppression for Onset Detection}, booktitle = {Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013)}, year = {2013}, month = {November}, address = {Curitiba, Brazil}, abstract = {

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.

}, keywords = {local group delay, onset detection, tremolo suppression, vibrato suppression}, author = {Sebastian B{\"o}ck and Widmer, Gerhard} } @conference {88, title = {Maximum Filter Vibrato Suppression for Onset Detection}, booktitle = {Proceedings of the 16th International Conference on Digital Audio Effects (DAFx-13)}, year = {2013}, month = {September}, address = {Maynooth, Ireland}, abstract = {

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.

}, keywords = {maximum filter, onset detection, vibrato suppression}, author = {Sebastian B{\"o}ck and Widmer, Gerhard} } @conference {111, title = {Musical Onset Detection with Convolutional Neural Networks}, booktitle = {Proceedings of the 6th International Workshop on Machine Learning and Music}, year = {2013}, month = {September}, address = {Prague, Czech Republic}, keywords = {convolutional neural networks, onset detection}, author = {Schl{\"u}ter, Jan and Sebastian B{\"o}ck} } @proceedings {116, title = {Rhytmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio}, journal = {Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013)}, year = {2013}, month = {November}, abstract = {

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.

}, author = {Krebs, Florian and Sebastian B{\"o}ck and Widmer, Gerhard} }