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Dr Tijl De Bie
Dr Tijl De Bie
BA, MSc, PhD(Leuven)
Tijl De Bie was appointed Lecturer in Artificial Intelligence at the University of Bristol in January 2007. Before that, he was a research assistant at the K.U.Leuven (Belgium) and the University of Southampton. He completed his PhD on machine learning and advanced optimization techniques in 2005 at the University of Leuven. During his PhD he spent research visits in Imperial College London, U.C. Berkeley, and U.C. Davis.
For more information please visit Tijl's research website.
I have two main areas of expertise on which I am also actively doing research:
- Music information retrieval, and in particular the design of computer algorithms that are able to understand music audio like a human music listener does. For example, such algorithms might understand the harmony in music, recognize the melody, feel the beats, etc. Additionally, I am working on algorithms that subsequently use this music understanding for recommending music, making personalized playlists, and manipulating music in creative ways.
- Data mining and knowledge discovery i.e. algorithms for the discovery of patterns and relations in large amounts of data.
data miningknowledge discoverymachine learningpattern analysismusic information retrieval
- Kang, B, Lijffijt, J, Santos-Rodriguez, R & De Bie, T, 2016, Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations. in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16. Association for Computing Machinery (ACM), New York, NY, USA, pp. 1615-1624
- Santos-Rodriguez, R, De Bie, T, Lijffijt, J & Kang, B, 2016, Informative data projections: a framework and two examples. in: European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). European Symposium on Artificial Neural Networks, Bruges (Belgium), pp. 635-640
- Santos-Rodriguez, R, De Bie, T & Mcvicar, M, 2016, Learning to separate vocals from polyphonic mixtures via ensemble methods and structured output prediction. in: Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. Institute of Electrical and Electronics Engineers (IEEE)
- Lijffijt, J, Spyropoulou, E, Kang, B & De Bie, T, 2015, P-N-RMiner: A Generic Framework for Mining Interesting Structured Relational Patterns. in: IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2015. Institute of Electrical and Electronics Engineers (IEEE)
- Mcvicar, M, Mesnage, C, Lijffijt, J & De Bie, T, 2015, Interactively exploring supply and demand in the UK independent music scene. in: Machine Learning and Knowledge Discovery in Databases, Part III. Springer LNCS
- Mesnage, C, Santos-Rodriguez, R, Mcvicar, M & De Bie, T, 2015, Trend Extraction on Twitter Time Series for Music Discovery.
- Mcvicar, M, Mesnage, C, De Bie, T, Lijffijt, J & Spyropoulou, E, 2015, Supply and demand of independent UK music artists on the web. in: Proceedings of the 2015 ACM Conference on Web Science.
- McVicar, M, Santos-Rodríguez, R, Ni, Y & De Bie, T, 2014, Automatic chord estimation from audio: A review of the state of the art. IEEE Transactions on Audio, Speech, and Language Processing, vol 22., pp. 556-575
- Ni, Y, McVicar, M, Santos-Rodriguez, R & De Bie, T, 2013, Understanding effects of subjectivity in measuring chord estimation accuracy. IEEE Transactions on Audio, Speech, and Language Processing, vol 21., pp. 2607-2615
- De Bie, T & Flach, PA, 2013, Guest editors' introduction: special section of selected papers from ECML-PKDD 2012. Data Mining and Knowledge Discovery, vol 27., pp. 442-443
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