| Is speech the new blood? recent progress in ai-based disease detection from audio in a nutshell M Milling, FB Pokorny, KD Bartl-Pokorny, BW Schuller Frontiers in digital health 4, 886615, 2022 | 25 | 2022 |
| Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach J Schaefer, M Milling, BW Schuller, B Bauer, JO Brunner, ... Science of the Total Environment 796, 148932, 2021 | 25 | 2021 |
| Emotion recognition in public speaking scenarios utilising an lstm-rnn approach with attention A Baird, S Amiriparian, M Milling, BW Schuller 2021 IEEE spoken language technology workshop (SLT), 397-402, 2021 | 19 | 2021 |
| Personalised depression forecasting using mobile sensor data and ecological momentary assessment A Kathan, M Harrer, L Küster, A Triantafyllopoulos, X He, M Milling, ... Frontiers in digital health 4, 964582, 2022 | 15 | 2022 |
| On the impact of word error rate on acoustic-linguistic speech emotion recognition: An update for the deep learning era S Amiriparian, A Sokolov, I Aslan, L Christ, M Gerczuk, T Hübner, ... arXiv preprint arXiv:2104.10121, 2021 | 14 | 2021 |
| Synchronization in interpersonal speech S Amiriparian, J Han, M Schmitt, A Baird, A Mallol-Ragolta, M Milling, ... Frontiers in Robotics and AI 6, 116, 2019 | 14 | 2019 |
| Emotion and themes recognition in music utilising convolutional and recurrent neural networks S Amiriparian, M Gerczuk, E Coutinho, A Baird, S Ottl, M Milling, ... | 13 | 2019 |
| Evaluating the impact of voice activity detection on speech emotion recognition for autistic children M Milling, A Baird, KD Bartl-Pokorny, S Liu, AM Alcorn, J Shen, ... Frontiers in Computer Science 4, 837269, 2022 | 10 | 2022 |
| Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations A Triantafyllopoulos, M Milling, K Drossos, BW Schuller arXiv preprint arXiv:2110.01506, 2021 | 7 | 2021 |
| Airborne pollen grain detection from partially labelled data utilising semi-supervised learning B Jin, M Milling, MP Plaza, JO Brunner, C Traidl-Hoffmann, BW Schuller, ... Science of The Total Environment 891, 164295, 2023 | 4 | 2023 |
| Automatic emotion modelling in written stories L Christ, S Amiriparian, M Milling, I Aslan, BW Schuller arXiv preprint arXiv:2212.11382, 2022 | 3 | 2022 |
| Depression diagnosis and forecast based on mobile phone sensor data X He, A Triantafyllopoulos, A Kathan, M Milling, T Yan, ST Rajamani, ... 2022 44th Annual International Conference of the IEEE Engineering in …, 2022 | 3 | 2022 |
| Journaling data for daily PHQ-2 depression prediction and forecasting A Kathan, A Triantafyllopoulos, X He, M Milling, T Yan, ST Rajamani, ... 2022 44th Annual International Conference of the IEEE Engineering in …, 2022 | 3 | 2022 |
| A Prototypical Network Approach for Evaluating Generated Emotional Speech}} A Baird, S Mertes, M Milling, L Stappen, T Wiest, E André, BW Schuller Proc. Interspeech 2021, 3161-3165, 2021 | 3 | 2021 |
| HEAR4Health: a blueprint for making computer audition a staple of modern healthcare A Triantafyllopoulos, A Kathan, A Baird, L Christ, A Gebhard, M Gerczuk, ... Frontiers in Digital Health 5, 1196079, 2023 | 2 | 2023 |
| Supervised contrastive learning for game-play frustration detection from speech M Song, E Parada-Cabaleiro, S Liu, M Milling, A Baird, Z Yang, ... International Conference on Human-Computer Interaction, 617-629, 2021 | 2 | 2021 |
| Predicting Sex and Stroke Success--Computer-aided Player Grunt Analysis in Tennis Matches L Stappen, M Milling, V Munst, K Hoffmann, BW Schuller arXiv preprint arXiv:2202.09102, 2022 | 1 | 2022 |
| Are you sure? Analysing Uncertainty Quantification Approaches for Real-world Speech Emotion Recognition O Schrüfer, M Milling, F Burkhardt, F Eyben, B Schuller arXiv preprint arXiv:2407.01143, 2024 | | 2024 |
| Beyond Deep Learning: Charting the Next Frontiers of Affective Computing A Triantafyllopoulos, L Christ, A Gebhard, X Jing, A Kathan, M Milling, ... Intelligent Computing, 2024 | | 2024 |
| An automatic analysis of ultrasound vocalisations for the prediction of interaction context in captive Egyptian fruit bats A Triantafyllopoulos, A Gebhard, M Milling, S Rampp, B Schuller arXiv preprint arXiv:2406.06332, 2024 | | 2024 |
Björn SchullerProfessor, Technische Universität München (TUM) / Imperial College London & CSO, audEERING在 tum.de 的电子邮件经过验证
