Music Information Retrieval

@article{parada:srep:2024,
  title={Song lyrics have become simpler and more repetitive over the last five decades},
  author={Parada-Cabaleiro, Emilia and Mayerl, Maximilian and Brandl, Stefan and Skowron, Marcin and Schedl, Markus and Lex, Elisabeth and Zangerle, Eva},
  journal={Scientific Reports},
  volume={14},
  number={1},
  pages={5531},
  year={2024},
  publisher={Nature Publishing Group UK London},
  doi={10.1038/s41598-024-55742-x}
}

@inproceedings{voetter@songpopordinal,
        author =       {V\"{o}tter, Michael and Mayerl, Maximilian and Zangerle, Eva and Specht, G\"{u}nther},
	title        = {Song Popularity Prediction using Ordinal Classification},
	year         = 2023,
	booktitle    = {Proceedings of the 20th Sound and Music Computing Conference. June 15-17, 2023. Stockholm, Sweden},
	publisher    = {Royal College of Music and KTH Royal Institute of Technology},
	editor       = {Roberto Bresin and Kjetil Falkenberg},
	organization = {Royal College of Music and KTH Royal Institute of Technology}
}

@inproceedings{mci/Mayerl2023,
author = {Mayerl, Maximilian AND Vötter, Michael AND Specht, Günther AND Zangerle, Eva},
title = {Pairwise Learning to Rank for Hit Song Prediction},
booktitle = {BTW 2023},
year = {2023},
editor = {König-Ries, Birgitta AND Scherzinger, Stefanie AND Lehner, Wolfgang AND Vossen, Gottfried} ,
doi = { 10.18420/BTW2023-26 },
publisher = {Gesellschaft für Informatik e.V.},
address = {}
}

@phdthesis{zangerle:habil:2022,
 author = {Eva Zangerle},
 school = {University of Innsbruck},
 title = {Recommender Systems for Music Retrieval Tasks},
 type = {Habilitation},
 year = {2022}
}

@article{voetter_ijsc_2022,
	title = {{HSP} {Datasets}: {Insights} on {Song} {Popularity} {Prediction}},
	issn = {1793-351X},
	url = {https://www.worldscientific.com/doi/abs/10.1142/S1793351X22400104},
	doi = {10.1142/S1793351X22400104},
	journal = {International Journal of Semantic Computing},
	author = {Vötter, Michael and Mayerl, Maximilian and Specht, Günther and Zangerle, Eva},
	month = may,
	year = {2022},
	note = {Publisher: World Scientific Publishing Co.},
	pages = {1--23},
}

@inproceedings{mayerlRecognizingSongMood2021,
	title = {Recognizing Song Mood and Theme: Clustering-based Ensembles},
	booktitle = {Working Notes Proceedings of the MediaEval 2021 Workshop},
	publisher = {ceur-ws.org},
	author = {Mayerl, Maximilian and Vötter, Michael and Peintner, Andreas and Specht, Günther and Zangerle, Eva},
	month = {12},
	year = {2021},
}

@inproceedings{voetter:ism:2021,
  author    = {
               Michael V\"{o}tter and Maximilian Mayerl and G\"{u}nther Specht and Eva Zangerle},
  title     = {Novel Datasets for Evaluating Song Popularity Prediction Tasks},
  booktitle = {{IEEE} International Symposium on Multimedia, {ISM} 2021, Virtual Event, November 29 -- December 1, 2021},
  pages     = {166--173},
  publisher = {{IEEE}},
  year      = {2021},
  doi       = {10.1109/ISM52913.2021.00034},
}

@article{pichl2020user,
  title={User models for multi-context-aware music recommendation},
  author={Pichl, Martin and Zangerle, Eva},
  journal={Multimedia Tools and Applications},
  pages={22509--22531},
  year={2021},
  publisher={Springer},
  doi={10.1007/s11042-020-09890-7},
  volume=80,
  number={15}
}

@ARTICLE{8382228,
  author={Eva {Zangerle} and Chih-Ming {Chen} and Ming-Feng {Tsai} and Yi-Hsuan {Yang}},
  journal={IEEE Transactions on Affective Computing}, 
  title={Leveraging Affective Hashtags for Ranking Music Recommendations}, 
  year={2021},
  volume={12},
  number={1},
  pages={78-91},
  doi={10.1109/TAFFC.2018.2846596}}

@article{kowald2021support,
  title={Support the underground: characteristics of beyond-mainstream music listeners},
  author={Kowald, Dominik and Muellner, Peter and Zangerle, Eva and Bauer, Christine and Schedl, Markus and Lex, Elisabeth},
  journal={EPJ Data Science},
  volume={10},
  number={1},
  pages={1--26},
  year={2021},
  publisher={Springer}
}

@proceedings{DBLP:conf/ismir/2020,
  author    = {Julie Cumming and
               Jin Ha Lee and
               Brian McFee and
               Markus Schedl and
               Johanna Devaney and
               Cory McKay and
               Eva Zangerle and
               Timothy de Reuse},
  title     = {Proceedings of the 21th International Society for Music Information
               Retrieval Conference, {ISMIR} 2020, Montreal, Canada, October 11-16,
               2020},
  year      = {2020},
  isbn      = {978-0-9813537-1-5},
  timestamp = {Mon, 19 Jul 2021 15:30:03 +0200},
  biburl    = {https://dblp.org/rec/conf/ismir/2020.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{zangerle2020user,
  title={User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues},
  author={Zangerle, Eva and Pichl, Martin and Schedl, Markus},
  journal={Transactions of the International Society for Music Information Retrieval},
  volume={3},
  number={1},
  year={2020},
  publisher={Ubiquity Press},
  doi={10.5334/tismir.37}
}

@inproceedings{zangerle_ismir20,
 author = {Liu, Meijun and Zangerle, Eva and Hu, Xiao and Melchiorre, Alessandro and Schedl, Markus},
 booktitle = {Proceedings of the 21st International Society for Music Information Retrieval Conference 2020 (ISMIR
2020)},
 month = {10},
 pages = {157-165},
 title = {Pandemics, Music, and Collective Sentiment: Evidence from the Outbreak of COVID-19},
 year = {2020}
}

@inproceedings{mediaeval2020,
 author = {Michael Vötter and Maximilian Mayerl and Günther Specht and Eva Zangerle},
 booktitle = {Working Notes Proceedings of the MediaEval 2020 Workshop},
 month = {12},
 publisher = {ceur-ws.org},
 title = {Recognizing Song Mood and Theme: Leveraging Ensembles of Tag Groups},
 year = {2020}
}

@inproceedings{10.1145/3383313.3412223,
 address = {New York, NY, USA},
 author = {Melchiorre, Alessandro B. and Zangerle, Eva and Schedl, Markus},
 booktitle = {14th ACM Conference on Recommender Systems (RecSys 2020)},
 doi = {10.1145/3383313.3412223},
 isbn = {9781450375832},
 keywords = {bias, dataset, music recommender systems, neural networks, personality},
 location = {Virtual Event, Brazil},
 numpages = {6},
 pages = {533–538},
 publisher = {ACM},
 series = {RecSys '20},
 title = {Personality Bias of Music Recommendation Algorithms},
 url = {https://doi.org/10.1145/3383313.3412223},
 year = {2020}
}

@inproceedings{mayerl2020comparing,
  title={Comparing Lyrics Features for Genre Recognition},
  author={Mayerl, Maximilian and V{\"o}tter, Michael and Moosleitner, Manfred and Zangerle, Eva},
  booktitle={Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)},
  pages={73--77},
  year={2020}
}

@inproceedings{zangerle_ismir19,
title = {{Hit Song Prediction: Leveraging Low- and High-Level Audio Features}},
author = {Eva Zangerle and Ramona Huber and Michael V\"{o}tter and  Yi-Hsuan Yang},
year = {2019},
pages = {319--326},
booktitle = {{Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019)}},
url = {http://archives.ismir.net/ismir2019/paper/000037.pdf}
}

@inproceedings{gvdb1019_1,
  author    = {Maximilian Mayerl and
               Michael V{\"{o}}tter and
               Eva Zangerle and
               G{\"{u}}nther Specht},
  title     = {Language Models for Next-Track Music Recommendation},
  booktitle = {Proceedings of the 31st GI-Workshop Grundlagen von Datenbanken, Saarburg,
               Germany, June 11-14, 2019.},
  pages     = {15--19},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2367/paper\_1.pdf},
}

@inproceedings{gvdb2019_2,
  author    = {Michael V{\"{o}}tter and
               Eva Zangerle and
               Maximilian Mayerl and
               G{\"{u}}nther Specht},
  title     = {Autoencoders for Next-Track-Recommendation},
  booktitle = {Proceedings of the 31st GI-Workshop Grundlagen von Datenbanken, Saarburg,
               Germany, June 11-14, 2019.},
  pages     = {20--25},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2367/paper\_2.pdf},
}

@inproceedings{mediaeval19_inn,
 author = {Maximilian Mayerl and Michael Vötter and Hsiao-Tzu Hung and Boyu Chen and Yi-Hsuan Yang and Eva Zangerle},
 booktitle = {Working Notes Proceedings of the MediaEval 2019 Workshop},
 month = {12},
 publisher = {ceur-ws.org},
 title = {Recognizing Song Mood and Theme Using Convolutional Recurrent Neural Networks},
 year = {2019}
}