Michael Vötter, MSc.

@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},
}

@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{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}
}

@inproceedings{mediaeval19_tai,
 author = {Hsiao-Tzu Hung and Yu-Hua Chen and Maximilian Mayerl and Michael Vötter and Eva Zangerle and Yi-Hsuan Yang},
 booktitle = {Working Notes Proceedings of the MediaEval 2019 Workshop},
 month = {12},
 publisher = {ceur-ws.org},
 title = {MediaEval 2019 Emotion and Theme Recognition task: A VQ-VAE Based Approach},
 year = {2019}
}