| Hit Song Prediction: Leveraging Low-and High-Level Audio Features. E Zangerle, M Vötter, R Huber, YH Yang ISMIR, 319-326, 2019 | 19 | 2019 |
| Recognizing Song Mood and Theme Using Convolutional Recurrent Neural Networks. M Mayerl, M Vötter, HT Hung, BY Chen, YH Yang, E Zangerle MediaEval, 2019 | 4 | 2019 |
| Comparing Lyrics Features for Genre Recognition M Mayerl, M Vötter, M Moosleitner, E Zangerle Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), 73-77, 2020 | 1 | 2020 |
| MediaEval 2019 Emotion and Theme Recognition task: A VQ-VAE Based Approach. HT Hung, YH Chen, M Mayerl, M Vötter, E Zangerle, YH Yang MediaEval 19, 27-29, 2019 | 1 | 2019 |
| Autoencoders for Next-Track-Recommendation. M Vötter, E Zangerle, M Mayerl, G Specht Grundlagen von Datenbanken, 20-25, 2019 | 1 | 2019 |
| Language Models for Next-Track Music Recommendation. M Mayerl, M Vötter, E Zangerle, G Specht Grundlagen von Datenbanken, 15-19, 2019 | 1 | 2019 |
| HSP Datasets: Insights on Song Popularity Prediction M Vötter, M Mayerl, G Specht, E Zangerle International Journal of Semantic Computing, 1-23, 2022 | | 2022 |
| Novel Datasets for Evaluating Song Popularity Prediction Tasks M Vötter, M Mayerl, G Specht, E Zangerle 2021 IEEE International Symposium on Multimedia (ISM), 166-173, 2021 | | 2021 |
| Recognizing Song Mood and Theme: Leveraging Ensembles of Tag Groups M Vötter, M Mayerl, G Specht, E Zangerle | | 2020 |
Eva ZangerleUniversität InnsbruckVerified email at uibk.ac.at
