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Michael Voetter
Michael Voetter
Verified email at uibk.ac.at
Title
Cited by
Cited by
Year
Hit Song Prediction: Leveraging Low-and High-Level Audio Features.
E Zangerle, M Vötter, R Huber, YH Yang
ISMIR, 319-326, 2019
192019
Recognizing Song Mood and Theme Using Convolutional Recurrent Neural Networks.
M Mayerl, M Vötter, HT Hung, BY Chen, YH Yang, E Zangerle
MediaEval, 2019
42019
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
12020
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
12019
Autoencoders for Next-Track-Recommendation.
M Vötter, E Zangerle, M Mayerl, G Specht
Grundlagen von Datenbanken, 20-25, 2019
12019
Language Models for Next-Track Music Recommendation.
M Mayerl, M Vötter, E Zangerle, G Specht
Grundlagen von Datenbanken, 15-19, 2019
12019
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
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