00462nas a2200157 4500008004100000245004900041210004900090260002800139100001800167700001600185700002000201700001500221700001500236700001700251856003600268 2015 eng d00aArtificial Intelligence in the Concertgebouw0 aArtificial Intelligence in the Concertgebouw aBuenos Aires, Argentina1 aArzt, Andreas1 aFrostel, H.1 aGadermaier, Th.1 aGasser, M.1 aWidmer, G.1 aGrachten, M. uhttp://phenicx.upf.edu/node/26500473nas a2200133 4500008004100000245007400041210006900115100001900184700001800203700002500221700002200246700002000268856005100288 2015 eng d00aClassical Music on the Web - User Interfaces and Data Representations0 aClassical Music on the Web User Interfaces and Data Representati1 aGasser, Martin1 aArzt, Andreas1 aGadermaier, Thassilo1 aGrachten, Maarten1 aWidmer, Gerhard uhttp://ismir2015.uma.es/articles/123_Paper.pdf00367nas a2200109 4500008004100000245006700041210006600108100001800174700001400192700001500206856003600221 2015 eng d00aFlexible Score Following: The Piano Music Companion and Beyond0 aFlexible Score Following The Piano Music Companion and Beyond1 aArzt, Andreas1 aGoebl, W.1 aWidmer, G. uhttp://phenicx.upf.edu/node/26800471nas a2200133 4500008004100000245008000041210006900121260002700190100002400217700002100241700001900262700002000281856003600301 2015 eng d00aI-Vectors for Timbre-Based Music Similarity and Music Artist Classification0 aIVectors for TimbreBased Music Similarity and Music Artist Class aMalaga, SpaincOctober1 aEghbal-zadeh, Hamid1 aLehner, Bernhard1 aSchedl, Markus1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/25600349nas a2200097 4500008004100000245007200041210006900113100001800182700001500200856003600215 2015 eng d00aReal-time Music Tracking using Multiple Performances as a Reference0 aRealtime Music Tracking using Multiple Performances as a Referen1 aArzt, Andreas1 aWidmer, G. uhttp://phenicx.upf.edu/node/26700430nas a2200121 4500008004100000245006600041210006500107260003700172100002400209700001900233700002000252856003600272 2015 eng d00aTimbral Modeling for Music Artist Recognition Using I-vectors0 aTimbral Modeling for Music Artist Recognition Using Ivectors aNice, FrancecAugust–September1 aEghbal-zadeh, Hamid1 aSchedl, Markus1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/25800431nas a2200121 4500008004100000245008000041210006900121260002400190100001600214700002800230700001500258856003600273 2014 eng d00aAnalysis and prediction of expressive dynamics using Bayesian linear models0 aAnalysis and prediction of expressive dynamics using Bayesian li aVenice, ItalycJuly1 aGrachten, M1 aChacón, C., E. Cancino1 aWidmer, G. uhttp://phenicx.upf.edu/node/15700494nas a2200133 4500008004100000245011300041210006900154260002400223100001700247700002100264700001900285700002000304856003600324 2014 eng d00aBridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio0 aBridging the AudioSymbolic Gap The Discovery of Repeated Note Co aLondon, UKc01/20141 aCollins, Tom1 aBöck, Sebastian1 aKrebs, Florian1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/13000473nas a2200157 4500008004100000245004800041210004300089260002400132100001800156700002100174700002500195700002000220700001900240700002000259856003600279 2014 eng d00aThe Complete Classical Music Companion V0.90 aComplete Classical Music Companion V09 aLondon, UKc01/20141 aArzt, Andreas1 aBöck, Sebastian1 aFlossmann, Sebastian1 aFrostel, Harald1 aGasser, Martin1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/13100429nas a2200121 4500008004100000245007900041210006900120260002800189100001700217700001700234700002000251856003600271 2014 eng d00aPatternViewer: An Application for Exploring Repetitive and Tonal Structure0 aPatternViewer An Application for Exploring Repetitive and Tonal aTaipei, TaiwancOctober1 aNikrang, Ali1 aCollins, Tom1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/18300419nas a2200157 4500008004100000245003000041210002600071100001800097700002100115700001800136700001600154700001500170700002500185700001500210856003600225 2014 eng d00aThe Piano Music Companion0 aPiano Music Companion1 aArzt, Andreas1 aBöck, Sebastian1 aFlossmann, S.1 aFrostel, H.1 aGasser, M.1 aLiem, Cynthia, C. S.1 aWidmer, G. uhttp://phenicx.upf.edu/node/27000391nas a2200109 4500008004100000245008200041210006900123100001800192700001500210700002000225856003600245 2014 eng d00aTempo- and Transposition-invariant Identification of Piece and Score Position0 aTempo and Transpositioninvariant Identification of Piece and Sco1 aArzt, Andreas1 aWidmer, G.1 aSonnleitner, R. uhttp://phenicx.upf.edu/node/26900378nas a2200097 4500008004100000245009000041210006900131260002400200100002000224856003600244 2014 eng d00aWhat Really Moves Us in Music: Expressivity as a Challenge to Semantic Audio Research0 aWhat Really Moves Us in Music Expressivity as a Challenge to Sem aLondon, UKc01/20141 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/12901707nas a2200145 4500008004100000245007400041210006900115260003100184520123100215100002201446700001901468700001801487700002001505856003601525 2013 eng d00aAutomatic alignment of music performances with structural differences0 aAutomatic alignment of music performances with structural differ aCuritiba, BrazilcNovember3 a
Both in interactive music listening, and in music performance research, there is a need for automatic alignment of different recordings of the same musical piece. This task is challenging, because musical pieces often contain parts that may or may not be repeated by the performer, possibly leading to structural differences between performances (or between performance and score). The most common alignment method, dynamic time warping (DTW), cannot handle structural differences adequately, and existing approaches to deal with structural differences explicitly rely on the annotation of ``break points'' in one of the sequences. We propose a simple extension of the Needleman-Wunsch algorithm to deal effectively with structural differences, without relying on annotations. We evaluate several audio features for alignment, and show how an optimal value can be found for the cost-parameter of the alignment algorithm. A single cost value is demonstrated to be valid across different types of music. We demonstrate that our approach yields roughly equal alignment accuracies compared to DTW in the absence of structural differences, and superior accuracies when structural differences occur.
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.
10aonset detection10apeak-picking1 aBöck, Sebastian1 aSchlüter, Jan1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/11001409nas 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 aWe 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/8801821nas a2200217 4500008004100000245008100041210006900122260003100191520117600222100001501398700001701413700001701430700001401447700001501461700001901476700002501495700001801520700001501538700001501553856003501568 2013 eng d00aPHENICX: Performances as Highly Enriched aNd Interactive Concert Experiences0 aPHENICX Performances as Highly Enriched aNd Interactive Concert aStockholm, Swedenc08/20133 aModern digital multimedia and internet technology have radically changed the ways people find entertainment and discover new interests online, seemingly without any phys- ical or social barriers. Such new access paradigms are in sharp contrast with the traditional means of entertainment. An illustrative example of this is live music concert perfor- mances that are largely being attended by dedicated audi- ences only.
This papers introduces the PHENICX project, which aims at enriching traditional concert experiences by using state- of-the-art multimedia and internet technologies. The project focuses on classical music and its main goal is twofold: (a) to make live concerts appealing to potential new au- dience and (b) to maximize the quality of concert experi- ence for everyone. Concerts will then become multimodal, multi-perspective and multilayer digital artifacts that can be easily explored, customized, personalized, (re)enjoyed and shared among the users. The paper presents the main scientific objectives on the project, provides a state of the art review on related research and presents the main chal- lenges to be addressed.
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.
1 aKrebs, Florian1 aBöck, Sebastian1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/11601078nas a2200145 4500008004100000245008500041210006900126260002100195520060000216100002400816700001900840700001800859700002000877856003500897 2013 eng d00aTracking Rests and Tempo Changes: Improved Score Following with Particle Filters0 aTracking Rests and Tempo Changes Improved Score Following with P aPerth, Australia3 aIn this paper we present a score following system based on a Dynamic Bayesian Network, using particle filtering as inference method. The proposed model sets itself apart from existing approaches by including two new extensions: A multi-level tempo model to improve alignment quality of performances with challenging tempo changes, and an extension to reflect different expressive characteristics of notated rests. Both extensions are evaluated against a dataset of classical piano music. As the results show, the extensions improve both the accuracy and the robustness of the algorithm.
1 aKorzeniowski, Filip1 aKrebs, Florian1 aArzt, Andreas1 aWidmer, Gerhard uhttp://phenicx.upf.edu/node/90