TU Wien Informatics

Overview Professors Scientific Staff Administrative Staff Student Staff External Lecturers By Responsibilities By Committee By Name
Related
Guest Professors Honoraries Alumni (LinkedIn) #5qw

Peter Knees

Univ.Prof. Dipl.-Ing. Dr.techn.

Research Focus

  • Information Systems Engineering: 70%
  • Visual Computing and Human-Centered Technology: 30%

Research Areas

  • Information Retrieval, User interfaces, Music Information Retrieval, Recommender Systems, Artificial Intelligence, Machine Learning
Peter Knees

Roles

Contact

2025W

2026S

 

2025

2024

2023

  • Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation / 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
    Download: PDF (486 KB)
    Project: HumRec (2021–2025)
  • Digital Humanism and Norms in Recommender Systems / 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
    Download: PDF (191 KB)
    Projects: CDL-RecSys (2022–2028) / HumRec (2021–2025)
  • Exploring Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation / 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
    Download: PDF (461 KB)
    Project: CDL-RecSys (2022–2028)
  • MuRS: Music Recommender Systems Workshop / 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
    Download: PDF (421 KB)
    Project: HumRec (2021–2025)
  • Connecting Ethnomusicology Data Collections Using Distributed Repositories and Linked Data Technology / 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
    Download: PDF (2.18 MB)
  • MILC 2023: 3rd Workshop on Intelligent Music Interfaces for Listening and Creation / 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
    Download: PDF (409 KB)
    Project: HumRec (2021–2025)
  • Digital Humanism: The Time Is Now / 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
    Project: DigHum Roadmap (2021–2023)
  • Causality Prediction with Neural-Symbolic Systems: A Case Study in Smart Grids / 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
    Download: PDF (2.48 MB)
    Project: HumRec (2021–2025)
  • Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness / 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
    Projects: CDL-RecSys (2022–2028) / HumRec (2021–2025)
  • Listener awareness in music recommender systems: directions and current trends / 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
    Project: HumRec (2021–2025)

2022

  • On the Impact and Interplay of Input Representations and Network Architectures for Automatic Music Tagging / 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
    Download: PDF (655 KB)
    Project: HumRec (2021–2025)
  • A Reproducibility Study on User-centric MIR Research and Why it is Important / 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
    Download: Camera-ready version (150 KB)
    Project: HumRec (2021–2025)
  • Bias and Feedback Loops in Music Recommendation: Studies on Record Label Impact / 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
    Download: Camera-ready version (1.13 MB)
    Project: HumRec (2021–2025)
  • Music Information Retrieval and Recommendation: Recent and Future Developments / 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).
  • ReStyle-MusicVAE: Enhancing User Control of Deep Generative Music Models with Expert Labeled Anchors / 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
    Project: HumRec (2021–2025)
  • Scaling Up Broken Systems? Considerations from the Area of Music Streaming / 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

2021

2020

2019

  • Preface to the 2nd Workshop on Intelligent Music Interfaces for Listening and Creation (MILC) / 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
  • Predicting user demographics from music listening information / 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
    Project: SmarterJam (2017–2019)
  • 9. User awareness in music recommender systems / 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
  • Intelligent music interfaces for listening and creation / 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
  • Intelligent User Interfaces for Music Discovery: The Past 20 Years and What's to Come / 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
  • Ethnomusicology meets Music Information Retrieval: Towards Automated Segmentation and Analysis / 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
  • Introduction to Music Information Retrieval / 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
  • Towards an alliance for distributed music data? / 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
  • An automatic drum machine with touch UI based on a generative neural network / 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
  • Recommenders and Intelligent Tools in Music Creation: Why, Why Not, and How? / 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
  • Music Information Retrieval: Inside and Outside the Music / 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
  • From a Critical Take on Music Recommendation to Digital Humanism / 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
  • On Stakeholders and Data Biases in Music Recommendation / 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
  • New Intelligent Tools in Music Creation? A Case for User-Centric Research / 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
  • Die perfekte Musikempfehlung - Perfekt für wen? / Knees, P. (2019). Die perfekte Musikempfehlung - Perfekt für wen? Future Music Camp 2019, Mannheim, Germany. http://hdl.handle.net/20.500.12708/87005
  • Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other / 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
  • A Proposal for a Neutral Music Recommender System / 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
  • Towards Uncovering Dataset Biases: Investigating Record Label Diversity in Music Playlists / 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
  • RecSys challenge 2019 / 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
  • Multi-Task Music Representation Learning from Multi-Label Embeddings / 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
  • Artificial Intelligence and the Future of Business / 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
  • Towards Recommender Systems for Music Creators / Knees, P. (2019). Towards Recommender Systems for Music Creators. HCI Lunchtime Scientific Series, TU Wien, Austria. http://hdl.handle.net/20.500.12708/87002
  • Introduction to Music Information Retrieval / Knees, P. (2019). Introduction to Music Information Retrieval. Informatik BEGINNER’s Day 2019, TU Wien, Austria. http://hdl.handle.net/20.500.12708/87001
  • RecSys Challenge '19: Proceedings of the Workshop on ACM Recommender Systems Challenge / 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

2018

2017

  • UMAP 2017 Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems / 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. UMAP’17 - 25th Conference on User Modeling, Adaptation and Personalization, Bratislava, Slovakia. ACM. https://doi.org/10.1145/3099023.3099095
  • MIREX Submission for Drum Transcription 2017 / 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
    Project: SmarterJam (2017–2019)
  • Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors / 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
  • Drum transcription from polyphonic music with recurrent neural networks / 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
    Project: SmarterJam (2017–2019)
  • An Intelligent Drum Machine for Electronic Dance Music Production and Performance / 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
    Project: SmarterJam (2017–2019)
  • Drum Transcription via Joint Beat and Drum Modeling Using Convolutional Recurrent Neural Networks / 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
    Project: SmarterJam (2017–2019)
  • Towards Visual Approaches to Audio Retrieval in Creative Music Production / 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
  • Introduction to Music Information Retrieval and Music Content Analysis / 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
  • Building Physical Props for Imagining Future Recommender Systems / 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
    Project: SmarterJam (2017–2019)
  • Prediction of User Demographics from Music Listening Habits / 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
  • New Paths in Music Recommender Systems Research / 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
    Project: SmarterJam (2017–2019)
  • Music Genre Classification Revisited: An In-Depth Examination Guided by Music Experts / 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
    Project: SmarterJam (2017–2019)
  • On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique / 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
    Project: SmarterJam (2017–2019)
  • Music Retrieval and Recommendation: Applications in Music Creation and Collaboration / 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
    Project: SmarterJam (2017–2019)
  • Music Information Retrieval for Creative Audio Production / 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
  • Introduction to Music Information Retrieval / Knees, P. (2017). Introduction to Music Information Retrieval. Informatik BEGINNER’s Day 2017, TU Wien, Austria. http://hdl.handle.net/20.500.12708/86640
  • Me, Myself & A.I. / Knees, P. (2017). Me, Myself & A.I. Werbeplanung.at Summit 2017, Vienna, Austria. http://hdl.handle.net/20.500.12708/86639

2004