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Werthner, H., Stanger, A., Schiaffonati, V., Knees, P., Hardman, L., & Ghezzi, C. (2023). Digital Humanism: The Time Is Now. Computer, 56(1), 138–142. https://doi.org/10.1109/MC.2022.3219528 ( reposiTUm)

Lerch, A., & Knees, P. (2021). Machine Learning Applied to Music/Audio Signal Processing. Electronics, 10(24), 3077. https://doi.org/10.3390/electronics10243077 ( reposiTUm)

Hofmann, A., Miksa, T., Knees, P., Bakos, A., Sağlam, H., Ahmedaja, A., Yimwadsana, B., Chan, C., & Rauber, A. (2021). Enabling FAIR use of Ethnomusicology Data - Through Distributed Repositories, Linked Data and Music Information Retrieval. Empirical Musicology Review, 16(1), 47–64. https://doi.org/10.18061/emr.v16i1.7632 ( reposiTUm)

Adamczak, J., Deldjoo, Y., Moghaddam, F. B., Knees, P., Leyson, G.-P., & Monreal, P. (2020). Session-based Hotel Recommendations Dataset: As part of the ACM Recommender System Challenge 2019. ACM Transactions on Intelligent Systems and Technology, 12(1), 1–20. https://doi.org/10.1145/3412379 ( reposiTUm)

Knees, P., Schedl, M., & Goto, M. (2020). Intelligent User Interfaces for Music Discovery. Transactions of the International Society for Music Information Retrieval, 3(1), 165–179. https://doi.org/10.5334/tismir.60 ( reposiTUm)

Tkalčič, M., Schedl, M., & Knees, P. (2020). Preface to the Special Issue on User Modeling for Personalized Interaction with Music. User Modeling and User-Adapted Interaction, 30(2), 195–198. https://doi.org/10.1007/s11257-020-09264-6 ( reposiTUm)

Krismayer, T., Schedl, M., Knees, P., & Rabiser, R. (2019). Predicting user demographics from music listening information. Multimedia Tools and Applications, 78(3), 2897–2920. https://doi.org/10.1007/s11042-018-5980-y ( reposiTUm)

Staudinger, M., Kern, B. M. J., Miksa, T., Arnhold, L., Knees, P., Rauber, A., & Hanbury, A. (2024). Mission Reproducibility: An Investigation on Reproducibility Issues in Machine Learning and Information Retrieval Research. In Proceedings 2024 IEEE 20th International Conference on e-Science (e-Science). IEEE eScience 2024, Osaka, Japan. IEEE. https://doi.org/10.1109/e-Science62913.2024.10678657 ( reposiTUm)

Ferraro, A., Porcaro, L., Knees, P., & Bauer, C. (2024). MuRS 2024: 2nd Music Recommender Systems Workshop. In T. Di Noia, P. Lops, T. Joachims, K. Verbert, P. Castells, Z. Dong, & B. London (Eds.), RecSys ’24: Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1202–1205). Association for Computing Machinery. https://doi.org/10.1145/3640457.3687097 ( reposiTUm)

Sowula, R., & Knees, P. (2024). Mosaikbox: Improving Fully Automatic DJ Mixing Through Rule-based Stem Modification And Precise Beat-Grid Estimation. In B. Kaneshiro, G. Mysore, O. Nieto, C. Donahue, C.-Z. A. Huang, J. H. Lee, B. McFee, & M. C. McCallum (Eds.), Proceedings of the 25th International Society for Music Information Retrieval Conference (pp. 850–857). International Society for Music Information Retrieva. https://doi.org/10.5281/zenodo.14877463 ( reposiTUm)

Seshadri, P., Shashaani, S., & Knees, P. (2024). Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning. In T. Di Noia, P. Lops, T. Joachims, K. Verbert, P. Castells, Z. Dong, & B. London (Eds.), RecSys ’24: Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1028–1032). Association for Computing Machinery. https://doi.org/10.1145/3640457.3688188 ( reposiTUm)

Sertkan, M., Althammer, S., Hofstätter, S., Knees, P., & Neidhardt, J. (2023). Exploring Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation. In Proceedings of the 3rd Workshop Perspectives on the Evaluation of Recommender Systems 2023 co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023). PERSPECTIVES 2023 - Perspectives on the Evaluation of Recommender Systems Workshop co-located with the 17th ACM Conference on Recommender Systems, Singapore, Singapore. CEUR-WS.org. https://doi.org/10.34726/5352 ( reposiTUm)

Ferraro, A., Knees, P., Quadrana, M., Ye, T., & Gouyon, F. (2023). MuRS: Music Recommender Systems Workshop. In J. Zhang, L. Chen, S. Berkovsky, J.-M. Zhang, T. Di Noia, J. Basilico, L. Pizzato, & Y. Song (Eds.), Proceedings of the Seventeenth ACM Conference on Recommender Systems, Singapore, 18th–22nd September 2023 (pp. 1227–1230). Association for Computing Machinery (ACM). https://doi.org/10.1145/3604915.3608750 ( reposiTUm)

Schreiberhuber, K., Sabou, M., Ekaputra, F. J., Knees, P., Aryan, P. R., Einfalt, A., & Mosshammer, R. (2023). Causality Prediction with Neural-Symbolic Systems: A Case Study in Smart Grids. In Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2023) (pp. 336–347). CEUR-WS.org. https://doi.org/10.34726/5300 ( reposiTUm)

Prem, E., Neidhardt, J., Knees, P., Woltran, S., & Werthner, H. (2023). Digital Humanism and Norms in Recommender Systems. In S. Vrijenhoek, L. Michiels, J. Kruse, A. Starke, J. Viader Guerrero, & N. Tintarev (Eds.), Proceedings of the First Workshop on the Normative Design and Evaluation of Recommender Systems. CEUR-WS.org. https://doi.org/10.34726/8560 ( reposiTUm)

Knees, P., & Lerch, A. (2023). MILC 2023: 3rd Workshop on Intelligent Music Interfaces for Listening and Creation. In Companion Proceedings of 2023 28th Annual Conference on Intelligent User Interfaces (IUI 2023 Companion) (pp. 185–186). Association for Computing Machinery. https://doi.org/10.1145/3581754.3584164 ( reposiTUm)

Seshadri, P., & Knees, P. (2023). Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation. In Proceedings of the Music Recommender Systems Workshop (MuRS) at the 17th ACM Recommender Systems Conference (RecSys’23). Music Recommender Systems Workshop at the 17th ACM Recommender Systems Conference (RecSys’23), Singapore, Singapore. Zenodo. https://doi.org/10.5281/zenodo.8372449 ( reposiTUm)

Damböck, M., Vogl, R., & Knees, P. (2022). On the Impact and Interplay of Input Representations and Network Architectures for Automatic Music Tagging. In P. Rao, H. Murphy, A. Srinivasamurthy, R. Bittner, R. Caro Repetto, M. Goto, X. Serra, & M. Miron (Eds.), Proceedings of the 23rd International Society for Music Information Retrieval Conference. ISMIR 2022 (pp. 941–948). International Society for Music Information Retrieval. https://doi.org/10.5281/zenodo.7343091 ( reposiTUm)

Prvulovic, D., Vogl, R., & Knees, P. (2022). ReStyle-MusicVAE: Enhancing User Control of Deep Generative Music Models with Expert Labeled Anchors. In A. Bellogin, L. Boratto, O. C. Santos, L. Ardissono, & B. Knijnenburg (Eds.), Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 63–66). Association for Computing Machinery. https://doi.org/10.1145/3511047.3536412 ( reposiTUm)

Knees, P., Ferwerda, B., Rauber, A., Strumbelj, S., Resch, A., Tomandl, L., Bauer, V., Tang, F. Y., Bobinac, J., Ceranic, A., & Dizdar, R. (2022). A Reproducibility Study on User-centric MIR Research and Why it is Important. In P. Rao, H. Murthy, A. Srinivasamurthy, R. Bittner, R. Caro Repetto, M. Goto, X. Serra, & M. Miron (Eds.), Proceedings of the 23rd International Society for Music Information Retrieval (ISMIR) Conference (pp. 764–771). International Society for Music Information Retrieval. https://doi.org/10.5281/zenodo.7316775 ( reposiTUm)

Knees, P., Ferraro, A., & Hübler, M. (2022). Bias and Feedback Loops in Music Recommendation: Studies on Record Label Impact. In H. Abdollahpouri, S. Sahebi, M. Elahi, M. Mansoury, B. Loni, Z. Nazari, & M. Dimakopoulou (Eds.), MORS 2022. Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems, co-located with 16th ACM Conference on Recommender Systems (RecSys 2022. CEUR-WS.org. https://doi.org/10.34726/3723 ( reposiTUm)

Knees, P. (2021). An Overview of Music Retrieval and Recommendation: From Describing Sound to Asking What is Fair. In M. Tkalcic, V. Pejović, M. Kljun, & K. Čopič Pucihar (Eds.), Proceedings of the 6th Human-Computer Interaction Slovenia Conference (p. 2). CEUR-WS. http://hdl.handle.net/20.500.12708/58756 ( reposiTUm)

Schindler, A., Gordea, S., & Knees, P. (2020). Unsupervised cross-modal audio representation learning from unstructured multilingual text. In Proceedings of the 35th Annual ACM Symposium on Applied Computing. 35th Annual ACM Symposium on Applied Computing (SAC ´20), Brno, Czechia. ACM. https://doi.org/10.1145/3341105.3374114 ( reposiTUm)

Hunold, S., Bhatele, A., Bosilca, G., & Knees, P. (2020). Predicting MPI Collective Communication Performance Using Machine Learning. In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE International Conference on Cluster Computing (IEEE Cluster 2020) - Online Conference, Kobe, Japan. IEEE. https://doi.org/10.1109/cluster49012.2020.00036 ( reposiTUm)

Schedl, M., Mayr, M., & Knees, P. (2020). Music Tower Blocks: Multi-Faceted Exploration Interface for Web-Scale Music Access. In Proceedings of the 2020 International Conference on Multimedia Retrieval. 2020 International Conference on Multimedia Retrieval (ICMR ´20), Dublin, Ireland. ACM. https://doi.org/10.1145/3372278.3391928 ( reposiTUm)

Böck, S., Davies, M., & Knees, P. (2019). Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other. In A. Flexer, G. Peeters, J. Urbano, & A. Volk (Eds.), Proceedings of the 20th International Society for Music Information Retrieval Conference (pp. 486–493). Zenodo. https://doi.org/10.5281/zenodo.3527849 ( reposiTUm)

Vogl, R., Eghbal-Zadeh, H., & Knees, P. (2019). An automatic drum machine with touch UI based on a generative neural network. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion. 24th International Conference on Intelligent User Interfaces, Marina del Rey, CA, United States of America (the). ACM. https://doi.org/10.1145/3308557.3308673 ( reposiTUm)

Knees, P., Deldjoo, Y., Moghaddam, F. B., Adamczak, J., Leyson, G.-P., & Monreal, P. (2019). RecSys challenge 2019. In Proceedings of the 13th ACM Conference on Recommender Systems. 13th ACM Conference on Recommender Systems, Copenhagen, Denmark. ACM. https://doi.org/10.1145/3298689.3346974 ( reposiTUm)

Schindler, A., & Knees, P. (2019). Multi-Task Music Representation Learning from Multi-Label Embeddings. In 2019 International Conference on Content-Based Multimedia Indexing (CBMI). 2019 International Conference on Content-Based Multimedia Indexing (CBMI), Dublin, Ireland. IEEE. https://doi.org/10.1109/cbmi.2019.8877462 ( reposiTUm)

Knees, P. (2019). A Proposal for a Neutral Music Recommender System. In M. Miron (Ed.), Proceedings of the 1st Workshop on Designing Human-Centric Music Information Research Systems (pp. 4–7). http://hdl.handle.net/20.500.12708/58098 ( reposiTUm)

Knees, P., & Hübler, M. (2019). Towards Uncovering Dataset Biases: Investigating Record Label Diversity in Music Playlists. In M. Miron (Ed.), Proceedings of the 1st Workshop on Designing Human-Centric Music Information Research Systems (pp. 19–22). http://hdl.handle.net/20.500.12708/58097 ( reposiTUm)

Knees, P., Schedl, M., & Goto, M. (2019). Intelligent User Interfaces for Music Discovery: The Past 20 Years and What’s to Come. In A. Flexer, G. Peeters, J. Urbano, & A. Volk (Eds.), Proceedings of the 20th International Society for Music Information Retrieval Conference (pp. 44–53). Zenodo. https://doi.org/10.5281/zenodo.3527737 ( reposiTUm)

Knees, P., Schedl, M., & Fiebrink, R. (2019). Intelligent music interfaces for listening and creation. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion. 24th International Conference on Intelligent User Interfaces, Marina del Rey, CA, United States of America (the). ACM. https://doi.org/10.1145/3308557.3313110 ( reposiTUm)

Knees, P., Schedl, M., & Fiebrink, R. (2019). Preface to the 2nd Workshop on Intelligent Music Interfaces for Listening and Creation (MILC). In C. Trattner, D. Parra, & N. Riche (Eds.), Joint Proceedings of the ACM IUI 2019 Workshops (p. 2). CEUR-WS.org. http://hdl.handle.net/20.500.12708/58093 ( reposiTUm)

Vogl, R., & Knees, P. (2018). MIREX Submission for Drum Transcription 2018. In 14th Music Information Retrieval Evaluation eXchange (MIREX 2018) (p. 2). International Music Information Retrieval Systems Evaluation Laboratory, School of Information Sciences, University of Illinois at Urbana-Champaign. http://hdl.handle.net/20.500.12708/56721 ( reposiTUm)

Knees, P., Schedl, M., & Fiebrink, R. (2018). IUI’18 Workshop on Intelligent Music Interfaces for Listening and Creation (MILC). In A. Said & T. Komatsu (Eds.), Joint Proceedings of the ACM IUI 2018 Workshops (p. 2). CEUR-WS.org. http://hdl.handle.net/20.500.12708/57621 ( reposiTUm)

Pálmason, H., Jónsson, B. Þ., Schedl, M., & Knees, P. (2018). Music Genre Classification Revisited: An In-Depth Examination Guided by Music Experts. In M. Aramaki, M. Davies, R. Kronland-Martinet, & S. Ystad (Eds.), Music Technology with Swing (pp. 49–62). Springer. https://doi.org/10.1007/978-3-030-01692-0_4 ( reposiTUm)

Deldjoo, Y., Schedl, M., Hidasi, B., & Knees, P. (2018). Multimedia recommender systems. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, Austria. ACM. https://doi.org/10.1145/3240323.3241620 ( reposiTUm)

Eghbal-Zadeh, H., Vogl, R., Widmer, G., & Knees, P. (2018). A GAN based Drum Pattern Generation UI Prototype. In 19th International Society for Music Information Retrieval Conference: Late-Breaking Demos Session (p. 2). http://hdl.handle.net/20.500.12708/57452 ( reposiTUm)

Vogl, R., Eghbal-Zadeh, H., Widmer, G., & Knees, P. (2018). GANs and Poses: An Interactive Generative Music Installation Controlled by Dance Moves. In 19th International Society for Music Information Retrieval Conference: Interactive Machine-Learning for Music @Exhibition (p. 5). http://hdl.handle.net/20.500.12708/57453 ( reposiTUm)

Vogl, R., Widmer, G., & Knees, P. (2018). Towards Multi-Instrument Drum Transcription. In Proceedings of the 21st International Conference on Digital Audio Effects (DAFx-18) (pp. 57–64). http://hdl.handle.net/20.500.12708/57451 ( reposiTUm)

Knees, P., Andersen, K., Said, A., & Tkalcic, M. (2017). UMAP 2017 Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. ACM. https://doi.org/10.1145/3099023.3099095 ( reposiTUm)

Vogl, R., Dorfer, M., Widmer, G., & Knees, P. (2017). MIREX Submission for Drum Transcription 2017. In 13th Music Information Retrieval Evaluation eXchange (MIREX 2017) (p. 1). International Music Information Retrieval Systems Evaluation Laboratory, School of Information Sciences, University of Illinois at Urbana-Champaign. http://hdl.handle.net/20.500.12708/57649 ( reposiTUm)

Krismayer, T., Schedl, M., Knees, P., & Rabiser, R. (2017). Prediction of User Demographics from Music Listening Habits. In Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing. 15th International Workshop on Content-Based Multimedia Indexing, Florenz, Italy. ACM. https://doi.org/10.1145/3095713.3095722 ( reposiTUm)

Knees, P., & Andersen, K. (2017). Building Physical Props for Imagining Future Recommender Systems. In Proceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces. 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, Limassol, Cyprus. ACM. https://doi.org/10.1145/3039677.3039682 ( reposiTUm)

Pálmason, H., Jónsson, B. Þ., Amsaleg, L., Schedl, M., & Knees, P. (2017). On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique. In C. Beeck, F. Borutta, P. Kröger, & T. Seidl (Eds.), Similarity Search and Applications (pp. 275–283). Lecture Notes in Computer Science, Springer. https://doi.org/10.1007/978-3-319-68474-1_19 ( reposiTUm)

Pálmason, H., Jónsson, B. T., Schedl, M., & Knees, P. (2017). Music Genre Classification Revisited: An In-Depth Examination Guided by Music Experts. In R. Kronland-Martinet, S. Ystad, & M. Aramaki (Eds.), Proceedings of the 13th International Symposium on Computer Music Multidisciplinary Research (pp. 45–56). Les éditions de PRISM. http://hdl.handle.net/20.500.12708/57190 ( reposiTUm)

Schedl, M., Knees, P., & Gouyon, F. (2017). New Paths in Music Recommender Systems Research. In Proceedings of the Eleventh ACM Conference on Recommender Systems. 11th ACM Conference on Recommender Systems, Como, Italy. ACM. https://doi.org/10.1145/3109859.3109934 ( reposiTUm)

Vogl, R., Dorfer, M., Widmer, G., & Knees, P. (2017). Drum Transcription via Joint Beat and Drum Modeling Using Convolutional Recurrent Neural Networks. In Proceedings of the 18th International Society for Music Information Retrieval Conference (pp. 150–157). http://hdl.handle.net/20.500.12708/57031 ( reposiTUm)

Vogl, R., & Knees, P. (2017). An Intelligent Drum Machine for Electronic Dance Music Production and Performance. In Proceedings of the 17th International Conference on New Interfaces for Musical Expression (pp. 231–236). http://hdl.handle.net/20.500.12708/57033 ( reposiTUm)

Vogl, R., Dorfer, M., & Knees, P. (2017). Drum transcription from polyphonic music with recurrent neural networks. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, United States of America (the). https://doi.org/10.1109/icassp.2017.7952146 ( reposiTUm)

Schedl, M., Lemmerich, F., Ferwerda, B., Skowron, M., & Knees, P. (2017). Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors. In 2017 IEEE International Symposium on Multimedia (ISM). 2017 IEEE International Symposium on Multimedia, Taichung, Taiwan (Province of China). IEEE. https://doi.org/10.1109/ism.2017.55 ( reposiTUm)

Knees, P., Schedl, M., Ferwerda, B., & Laplante, A. (2023). Listener awareness in music recommender systems: directions and current trends. In M. Augstein, E. Herder, & W. Wörndl (Eds.), Personalized Human-Computer Interaction (pp. 279–312). DeGruyter Oldenbourg. https://doi.org/10.1515/9783110988567-011 ( reposiTUm)

Knees, P., Neidhardt, J., & Nalis-Neuner, I. (2023). Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness. In H. Werthner, C. Ghezzi, & J. Kramer (Eds.), Introduction to Digital Humanism : A Textbook (pp. 417–434). Springer. https://doi.org/10.1007/978-3-031-45304-5_27 ( reposiTUm)

Knees, P. (2022). Scaling Up Broken Systems? Considerations from the Area of Music Streaming. In H. Werthner, E. Prem, E. A. Lee, & C. Ghezzi (Eds.), Perspectives on Digital Humanism (pp. 165–171). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-86144-5_23 ( reposiTUm)

Knees, P. (2021). Datengestützte Empfehlungssysteme – Kuratiertes Musikangebot. In H. Wandjo & A. Endreß (Eds.), Musikwirtschaft im Zeitalter der Digitalisierung (pp. 419–432). Nomos Verlagsgesellschaft mbH & Co. KG. https://doi.org/10.5771/9783845276939-419 ( reposiTUm)

Knees, P., Schedl, M., Ferwerda, B., & Laplante, A. (2019). 9. User awareness in music recommender systems. In M. Augstein, E. Herder, & W. Wörndl (Eds.), Personalized Human-Computer Interaction (pp. 223–252). DeGruyter. https://doi.org/10.1515/9783110552485-009 ( reposiTUm)

Eichinger, A., Knees, P., & Werthner, H. (Eds.). (2024). Digitalisierung und wir: Lehrbuch zum Digitalen Humanismus mit praktischen Übungen. Residenz. https://doi.org/10.34726/8506 ( reposiTUm)

Lerch, A., & Knees, P. (Eds.). (2021). Special Issue “Machine Learning Applied to Music/Audio Signal Processing.” MDPI. http://hdl.handle.net/20.500.12708/24931 ( reposiTUm)

Knees, P., & Gan, Z. (Eds.). (2020). The ACM Multimedia 2020 Interactive Arts Exhibition: Human and AI Generated Multimedia. GitHub. http://hdl.handle.net/20.500.12708/24762 ( reposiTUm)

Tkalcic, M., Schedl, M., & Knees, P. (Eds.). (2020). Special Issue on User Modeling for Personalized Interaction with Music. Springer Nature Switzerland AG. http://hdl.handle.net/20.500.12708/24932 ( reposiTUm)

Knees, P., Deldjoo, Y., Bakhshandegan Moghaddam, F., Adamczak, J., Leyson, G.-P., & Monreal, P. (Eds.). (2019). RecSys Challenge ’19: Proceedings of the Workshop on ACM Recommender Systems Challenge. ACM. https://doi.org/10.1145/3359555 ( reposiTUm)

Weise, M., Knees, P., Hofmann, A., Ahmedaja, A., Anda Beitāne, & Rauber, A. (2023, May 24). Connecting Ethnomusicology Data Collections Using Distributed Repositories and Linked Data Technology [Conference Presentation]. 3rd Conference of the Portuguese ICTM National Committee, Aveiro, Portugal. https://doi.org/10.34726/4323 ( reposiTUm)

Knees, P. (2022, August 22). Music Information Retrieval and Recommendation: Recent and Future Developments [Presentation]. Georgia Tech Center for Music Technology Seminar Series, Atlanta, GA, United States of America (the). ( reposiTUm)

Knees, P., Bauer, C., Lex, E., Sacharidis, D., & Tkalcic, M. (2021). Panel on: Human-centered AI - Are we there yet? 5th HUMANIZE Workshop on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory, College Station, TX, USA, United States of America (the). http://hdl.handle.net/20.500.12708/87299 ( reposiTUm)

Knees, P. (2021). Nachvollziehbare KI. Digitale Kompetenzen @ Parlament, Wien, Austria. http://hdl.handle.net/20.500.12708/87300 ( reposiTUm)

Knees, P. (2020). Neutrality and Fairness in Music Recommendation: A Matter of Digital Humanism. Georgia Tech Center for Music Technology Seminar Series, Atlanta, United States of America (the). http://hdl.handle.net/20.500.12708/87097 ( reposiTUm)

Knees, P. (2020). Music Information Retrieval for Building Intelligent Music Creation Tools. Interactive Music Technologies, St. Gilgen, Austria. http://hdl.handle.net/20.500.12708/87056 ( reposiTUm)

Knees, P. (2019). Introduction to Music Information Retrieval. Informatik BEGINNER’s Day 2019, TU Wien, Austria. http://hdl.handle.net/20.500.12708/87001 ( reposiTUm)

Knees, P. (2019). Towards Recommender Systems for Music Creators. HCI Lunchtime Scientific Series, TU Wien, Austria. http://hdl.handle.net/20.500.12708/87002 ( reposiTUm)

Knees, P. (2019). Introduction to Music Information Retrieval. 1st Workshop on Distributed Ethnomusicology Data and Music Information Retrieval, Mahidol University, Thailand. http://hdl.handle.net/20.500.12708/87003 ( reposiTUm)

Knees, P. (2019). Die perfekte Musikempfehlung - Perfekt für wen? Future Music Camp 2019, Mannheim, Germany. http://hdl.handle.net/20.500.12708/87005 ( reposiTUm)

Knees, P., Wasner, C., & Krenn, B. (2019). Artificial Intelligence and the Future of Business. Vienna International Business Club, Expat Center of the Vienna Business Agency, Austria. http://hdl.handle.net/20.500.12708/87004 ( reposiTUm)

Sağlam, H., Hofmann, A., Ahmedaja, A., Miksa, T., & Knees, P. (2019). Towards an alliance for distributed music data? 45th International Council for Traditional Music World Conference, Bangkok, Thailand. http://hdl.handle.net/20.500.12708/87007 ( reposiTUm)

Knees, P. (2019). Ethnomusicology meets Music Information Retrieval: Towards Automated Segmentation and Analysis. 2nd Workshop on Distributed Ethnomusicology data and MIR in the framework of ASEA-UNINET, Mahidol University, Thailand. http://hdl.handle.net/20.500.12708/87006 ( reposiTUm)

Bauer, C., Knees, P., Vogl, R., & Raber, H. (2019). Recommenders and Intelligent Tools in Music Creation: Why, Why Not, and How? Ars Electronica 2019 - AIxMusic Workshops, Linz, Austria. http://hdl.handle.net/20.500.12708/87008 ( reposiTUm)

Bauer, C., & Knees, P. (2019). Music Information Retrieval: Inside and Outside the Music. Ars Electronica 2019 - AIxMusic Matinée, Linz, Austria. http://hdl.handle.net/20.500.12708/87009 ( reposiTUm)

Knees, P. (2019). New Intelligent Tools in Music Creation? A Case for User-Centric Research. User Experience Design, Jönköping University, Sweden. http://hdl.handle.net/20.500.12708/87010 ( reposiTUm)

Knees, P. (2019). On Stakeholders and Data Biases in Music Recommendation. Media Technology and Interaction Design Seminar, KTH Stockholm, Sweden. http://hdl.handle.net/20.500.12708/87011 ( reposiTUm)

Knees, P. (2019). From a Critical Take on Music Recommendation to Digital Humanism. Critical Perspectives on Data Science and Machine Learning, KTH Stockholm, Sweden. http://hdl.handle.net/20.500.12708/87012 ( reposiTUm)

Knees, P. (2018). Music and Sound Recommendation for Listeners and Creators. eBusiness in the Creative Industries, University of Vienna, Austria. http://hdl.handle.net/20.500.12708/86813 ( reposiTUm)

Knees, P. (2018). Selected Topics in Music Recommendation. E-Commerce Research Seminar for Ph.D. Students 2018W, TU Wien, Austria. http://hdl.handle.net/20.500.12708/86815 ( reposiTUm)

Knees, P. (2018). Künstliche Intelligenz als personalisierter Komponist - Automatische Musikerzeugung als das Ende der Tantiemen? Future Music Camp 2018, Mannheim, Germany. http://hdl.handle.net/20.500.12708/86812 ( reposiTUm)

Schedl, M., Knees, P., & Gouyon, F. (2018). Overview and New Challenges of Music Recommendation Research in 2018. 19th International Society for Music Information Retrieval Conference, Paris, France. http://hdl.handle.net/20.500.12708/86810 ( reposiTUm)

Knees, P. (2018). Towards Visual Interfaces to Sound and Music Retrieval. National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan. http://hdl.handle.net/20.500.12708/86811 ( reposiTUm)

Knees, P. (2017). Me, Myself & A.I. Werbeplanung.at Summit 2017, Vienna, Austria. http://hdl.handle.net/20.500.12708/86639 ( reposiTUm)

Knees, P. (2017). Introduction to Music Information Retrieval. Informatik BEGINNER’s Day 2017, TU Wien, Austria. http://hdl.handle.net/20.500.12708/86640 ( reposiTUm)

Knees, P., & Vogl, R. (2017). Music Information Retrieval for Creative Audio Production. i²c Networking Friday 2017, TU Wien, Austria. http://hdl.handle.net/20.500.12708/86649 ( reposiTUm)

Knees, P. (2017). Music Retrieval and Recommendation: Applications in Music Creation and Collaboration. 3rd Berlin Music Information Retrieval Meetup, Berlin, Germany. http://hdl.handle.net/20.500.12708/86636 ( reposiTUm)

Knees, P. (2017). Towards Visual Approaches to Audio Retrieval in Creative Music Production. Guest Lecture, Bangkok, Thailand. http://hdl.handle.net/20.500.12708/86637 ( reposiTUm)

Knees, P. (2017). Introduction to Music Information Retrieval and Music Content Analysis. Indonesian Summer School on Music Information Retrieval, Depok, Indonesia. http://hdl.handle.net/20.500.12708/86638 ( reposiTUm)

Knees, P. (2020). Information retrieval and recommender systems for music listening and creation [Professorial Dissertation, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/158884 ( reposiTUm)