

Michael Vötter, Maximilian Mayerl, Günther Specht and Eva Zangerle: HSP Datasets: Insights on Song Popularity Prediction. In International Journal of Semantic Computing, pages 1-23. 2022 Publisher: World Scientific Publishing Co.
@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}, }
Maximilian Mayerl, Michael Vötter, Andreas Peintner, Günther Specht and Eva Zangerle: Recognizing Song Mood and Theme: Clustering-based Ensembles. In Working Notes Proceedings of the MediaEval 2021 Workshop. ceur-ws.org, 2021
@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}, }
Michael Vötter, Maximilian Mayerl, Günther Specht and Eva Zangerle: Novel Datasets for Evaluating Song Popularity Prediction Tasks. In IEEE International Symposium on Multimedia, ISM 2021, Virtual Event, November 29 - December 1, 2021, pages 166-173. IEEE, 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}, }
Michael Vötter, Maximilian Mayerl, Günther Specht and Eva Zangerle: Recognizing Song Mood and Theme: Leveraging Ensembles of Tag Groups. In Working Notes Proceedings of the MediaEval 2020 Workshop. ceur-ws.org, 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} }
Maximilian Mayerl, Michael Vötter, Manfred Moosleitner and Eva Zangerle: Comparing Lyrics Features for Genre Recognition. In Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), pages 73-77. 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} }
Eva Zangerle, Ramona Huber, Michael Vötter and Yi-Hsuan Yang: Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019), pages 319-326. 2019
@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} }
Maximilian Mayerl, Michael Vötter, Eva Zangerle and Günther Specht: Language Models for Next-Track Music Recommendation. In Proceedings of the 31st GI-Workshop Grundlagen von Datenbanken, Saarburg, Germany, June 11-14, 2019., pages 15-19. 2019
@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}, }
Michael Vötter, Eva Zangerle, Maximilian Mayerl and Günther Specht: Autoencoders for Next-Track-Recommendation. In Proceedings of the 31st GI-Workshop Grundlagen von Datenbanken, Saarburg, Germany, June 11-14, 2019., pages 20-25. 2019
@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}, }
Maximilian Mayerl, Michael Vötter, Hsiao-Tzu Hung, Boyu Chen, Yi-Hsuan Yang and Eva Zangerle: Recognizing Song Mood and Theme Using Convolutional Recurrent Neural Networks. In Working Notes Proceedings of the MediaEval 2019 Workshop. ceur-ws.org, 2019.
@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} }
Hsiao-Tzu Hung, Yu-Hua Chen, Maximilian Mayerl, Michael Vötter, Eva Zangerle and Yi-Hsuan Yang: MediaEval 2019 Emotion and Theme Recognition task: A VQ-VAE Based Approach. In Working Notes Proceedings of the MediaEval 2019 Workshop. ceur-ws.org, 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} }