Markus Schedl

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Peter KneesFaculty of Informatics, TU WienVerified email at tuwien.ac.at
Gerhard WidmerProfessor of Computer Science, Johannes Kepler University LinzVerified email at jku.at
Marko TkalcicUniversity of PrimorskaVerified email at famnit.upr.si
Bruce FerwerdaAssistant Professor, Jönköping UniversityVerified email at ju.se
Yashar DeldjooAssistant Professor (RtD-b) of Computer Science, Polytechnic University of Bari, ItalyVerified email at acm.org
Bogdan IonescuPolitehnica University of BucharestVerified email at upb.ro
Andreu VallJohannes Kepler UniversityVerified email at jku.at
Dominik SchnitzerAustrian Research Institute for Artificial Intelligence (OFAI)Verified email at ofai.at
Christine BauerUtrecht UniversityVerified email at uu.nl
Emilia GómezJoint Research Centre, European Commission and Music Technology Group, Universitat Pompeu FabraVerified email at upf.edu
Julián UrbanoTU DelftVerified email at tudelft.nl
Paolo CremonesiDept. Computer Engineering, Politecnico di MilanoVerified email at polimi.it
Hamid Eghbal-zadehMetaVerified email at meta.com
Sebastian BöckenliteAIVerified email at enlite.ai
Cynthia C. S. LiemAssociate Professor, Multimedia Computing Group, Delft University of TechnologyVerified email at tudelft.nl
Mats SjöbergMachine learning specialist at CSCVerified email at csc.fi
Arthur FlexerJohannes Kepler University LinzVerified email at jku.at
Hamed ZamaniAssistant Professor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Ching-Wei ChenSpotifyVerified email at cwlabs.com
Marcin SkowronResearch Scientist | Austrian Research Institute for AI | Johannes Kepler UniversityVerified email at ofai.at

Markus Schedl

Professor at Johannes Kepler University Linz, Institute of Computational Perception

Verified email at jku.at - Homepage

Recommender Systems Information Retrieval Multimedia Fairness Human-centered AI


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Music recommender systems M Schedl, P Knees, B McFee, D Bogdanov, M Kaminskas Recommender systems handbook, 453-492, 2015	412*	2015
Music information retrieval: Recent developments and applications M Schedl, E Gómez, J Urbano Foundations and Trends® in Information Retrieval 8 (2-3), 127-261, 2014	287	2014
Current challenges and visions in music recommender systems research M Schedl, H Zamani, CW Chen, Y Deldjoo, M Elahi International Journal of Multimedia Information Retrieval 7 (2), 95-116, 2018	258	2018
Polyphonic piano note transcription with recurrent neural networks S Böck, M Schedl 2012 IEEE international conference on acoustics, speech and signal …, 2012	183	2012
The LFM-1b Dataset for Music Retrieval and Recommendation M Schedl Proceedings of the 2016 ACM on international conference on multimedia …, 2016	173	2016
Evaluating the Online Capabilities of Onset Detection Methods S Böck, F Krebs, M Schedl ISMIR, 49-54, 2012	162	2012
A survey of music similarity and recommendation from music context data P Knees, M Schedl ACM Transactions on Multimedia Computing, Communications, and Applications …, 2013	161	2013
The neglected user in music information retrieval research M Schedl, A Flexer, J Urbano Journal of Intelligent Information Systems 41 (3), 523-539, 2013	158	2013
Fusing social media cues: personality prediction from twitter and instagram M Skowron, M Tkalčič, B Ferwerda, M Schedl Proceedings of the 25th international conference companion on world wide web …, 2016	153	2016
An innovative three-dimensional user interface for exploring music collections enriched P Knees, M Schedl, T Pohle, G Widmer Proceedings of the 14th ACM international conference on Multimedia, 17-24, 2006	136	2006
A music search engine built upon audio-based and web-based similarity measures P Knees, T Pohle, M Schedl, G Widmer Proceedings of the 30th annual international ACM SIGIR conference on …, 2007	131	2007
Enhanced beat tracking with context-aware neural networks S Böck, M Schedl Proc. Int. Conf. Digital Audio Effects, 135-139, 2011	126	2011
Location-aware music recommendation using auto-tagging and hybrid matching M Kaminskas, F Ricci, M Schedl Proceedings of the 7th ACM Conference on Recommender Systems, 17-24, 2013	120	2013
Predicting personality traits with instagram pictures B Ferwerda, M Schedl, M Tkalcic Proceedings of the 3rd Workshop on Emotions and Personality in Personalized …, 2015	119	2015
Local and global scaling reduce hubs in space. D Schnitzer, A Flexer, M Schedl, G Widmer Journal of Machine Learning Research 13 (10), 2012	114	2012
On Rhythm and General Music Similarity. T Pohle, D Schnitzer, M Schedl, P Knees, G Widmer ISMIR, 525-530, 2009	108	2009
Using instagram picture features to predict users’ personality B Ferwerda, M Schedl, M Tkalcic International conference on multimedia modeling, 850-861, 2016	98	2016
Using block-level features for genre classification, tag classification and music similarity estimation K Seyerlehner, M Schedl, T Pohle, P Knees Submission to Audio Music Similarity and Retrieval Task of MIREX 2010 (2), 3, 2010	98	2010
The MediaEval 2015 Affective Impact of Movies Task. M Sjöberg, Y Baveye, H Wang, VL Quang, B Ionescu, E Dellandréa, ... MediaEval 1436, 2015	95	2015
Personality traits predict music taxonomy preferences B Ferwerda, E Yang, M Schedl, M Tkalcic Proceedings of the 33rd annual acm conference extended abstracts on human …, 2015	93	2015

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