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  1. Dr Tillman Weyde
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Contact Information

Contact

Visit Dr Tillman Weyde

A304C, College Building

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Postal Address

City, University of London
Northampton Square
London
EC1V 0HB
United Kingdom

About

Background

Tillman Weyde is a Senior Lecturer at the Department of Computing. Before that he was a researcher and coordinator of the MUSITECH project at the Research Department of Music and Media Technology at the University of Osnabrück. He holds degrees in Computer Science, Music, and Mathematics and obtained his PhD in Systematic Musicology on the topic of automatic analysis of rhythms based on knowledge and machine learning. He is an associated member of the Institute of Cognitive Science and the Research Department of Music and Media Technology of the University of Osnabrück and has given invited talks among others at the IRCAM, Paris, Technical University of Berlin and the University of Karlsruhe. He is co-author of the educational software "Computer Courses in Music Ear Training" Published by Schott Music, which received the Comenius Medal for Exemplary Educational Media in 2000 and co-editor of the Osnabrück Series on Music and Computation. Tillman was a consultant to the NEUMES project at Harvard University and he is a member of the MPEG Ad-Hoc-Group on Symbolic Music Representation (SMR), working on the integration of SMR into MPEG-4. He was the principal investigator at City in the music e-learning project i-Maestro which was supported by the
European Commission. He currently works on Semantic Web representations for music, methods for automatic music analysis, audio-based similarity and recommendation and general applications of audio processing and machine learning in industry and science.

Qualifications

PhD Systematic Musicology (Music Technology), University of Osnabrück, 2002
Staatsexamen (MSc) Computer Science, University of Osnabrück, 1999
Staatsexamen (MSc) Mathematics, Music, Philosophy & Pedagogy, University of Osnabrück, 1994

Employment

2005 - to date City University London, Senior Lecturer
2001 - 2005 University of Osnabrück, Researcher
1997 - 2001 University of Osnabrück, Assistant Researcher
1994 - 1997 University of Osnabrück, Assistant Researcher & Lecturer

Other appointments

2005 Consultant to the project Developing the infrastructure for a distributed e-library of medieval music transcription in standardised format based at Oxford University & funded by Eduserv.
2003 - 2004 Consultant to the Harvard based project NEUMES Digital Encoding of Medieval Chant funded by the Andrew E. Mellon foundation.
2000 Visiting Lecturer (part time) at University of Applied Sciences at the University of Osnabrück in Mathematics
1998 - 2001 Independent consultant on multimedia information & education systems.

Membership of professional bodies

2012 British Computer Society, Professional Member
2012 Society of Interdisciplinary Musicology, Member
2002 Gesellschaft für Informatik, Member
1998 International Cooperative on Systematic Musicology, Member

Research Students

Name
Calogero Lauricella
Attendance
Feb 2017 – present, part-time
Thesis Title
Predicting Investor Churn in the Fintech Industry
Role
1st Supervisor
Name
Can Koluman
Attendance
Oct 2016 – present, part-time
Thesis Title
Emotion Driven Machine Learning and Decision Making
Role
1st Supervisor
Name
Adiana Danilakova
Attendance
Sep 2016 – present, part-time
Thesis Title
Image Analysis with Machine Learning and Ontological Reasoning
Role
1st Supervisor
Name
Gissel Velarde
Attendance
Oct 2012 – present, full-time
Thesis Title
Convolutional methods for music analysis
Role
External Supervisor
Name
Reinier de Valk
Attendance
Oct 2011 – Sep 2015, full-time
Thesis Title
Cognitive Modelling of Polyphonic Structures in Lute Tablature
Role
1st Supervisor
Further Information
Research Area: Computational Musicology, Artificial Intelligence, Date of start 01 Oct 2011.
Name
Daniel Wolff
Attendance
Oct 2010 – Aug 2017, full-time
Thesis Title
Culture-aware Music Information Retrieva
Role
1st Supervisor
Further Information
Research Area Information Retrieval: Artificial Intelligence, Date of start 01 Oct 2010.
Name
Jens Wissmann
Attendance
Sep 2005 – Jul 2012, part-time
Thesis Title
Chord Sequence patterns in OWL
Role
1st Supervisor
Further Information
Completed 2012.
Name
Srikanth Cherla
Thesis Title
Deep neural networks for music analysis & prediction
Further Information
Research Area: Artificial Intelligence, Computational Musicology, Date of start 01 Oct 2012.
Name
Andrew Lambert
Thesis Title
Investigating the Biological Root of Musical Creativity with Self-organised Oscillator Synchronisation Models
Further Information
Research Area: Artificial Intelligence, Computation Creativity, Date of start 01 Oct 2013.
Name
Andreas Jansson
Thesis Title
Learning to recognise harmony in musical audio signals
Further Information
Research Area: Artificial Intelligence, Computational Musicology, Signal Processing, Date of start 01 Feb 2012.

Publications

Chapters (5)

  1. Weyde, T. and De Valk, R. (2016). Chord- and note-based approaches to voice separation. Computational Music Analysis (pp. 137–154). ISBN 978-3-319-25931-4.
  2. Velarde, G. and David, M. (2016). A wavelet-based approach to pattern discovery in melodies. Computational Music Analysis (pp. 303–333). ISBN 978-3-319-25931-4.
  3. Lambert, A., Weyde, T.E. and Armstrong, N. (2014). Beyond the Beat: Towards Metre, Rhythm and Melody Modelling with Hybrid
    Oscillator Networks.
    In Georgaki, A. and Kouroupetroglou, G. (Eds.), Music Technology Meets Philosophy: from Digital Echos to Virtual Ethos (pp. 485–490).
  4. Cherla, S., Weyde, T., Garcez, A.S.D. and Pearce, M. (2013). A Distributed Model For Multiple-Viewpoint Melodic Prediction. In Jr, A.D.S.B., Gouyon, F. and Dixon, S. (Eds.), Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013, Curitiba, Brazil, November 4-8, 2013 (pp. 15–20). ISBN 978-0-615-90065-0.
  5. Weyde, T.E. and Müssgens, B. (2003). Untersuchungen zum musikalischen Schrifterwerb (Studies in Musical Literacy). In Enders, B. and Stange-Elbe, J. (Eds.), Global Village, Global Brain, Global Music - KlangArt-Kongress 1999 (pp. 451–462). Osnabrück: epOs Publishing ISBN 978-3-923486-41-0.

Conference Papers and Proceedings (72)

  1. Weyde, T.E., Colton, S., Llano, M.T., Hepworth, R., Charnley, J., Gale, C., Baron, A., Pachet, F., Roy, P., Gérvas, P., Collins, N., Sturm, B., Wolff, D. and Lloyd, J.R. (2016). The Beyond the Fence Musical and Computer Says Show Documentary. 7th International Conference on Computational Creativity 27 Jun 2016 – 1 Jul 2016, Paris, France.
  2. Sarkar, S., Weyde, T., Garcez, A.D.A., Slabaugh, G., Dragicevic, S. and Percy, C. (2016). Accuracy and interpretability trade-offs in machine learning applied to safer gambling. .
  3. Percy, C., D'Avila Garcez, A.S., Dragicevic, S., França, M.V.M., Slabaugh, G. and Weyde, T. (2016). The need for knowledge extraction: Understanding harmful gambling behavior with neural networks. .
  4. Abdallah, S., Benetos, E., Gold, N., Hargreaves, S., Weyde, T. and Wolff, D. (2016). Digital music lab: A framework for analysing big music data. .
  5. Sigtia, S., Benetos, E., Boulanger-Lewandowski, N., Weyde, T., D'Avila Garcez, A.S. and Dixon, S. (2015). A hybrid recurrent neural network for music transcription. .
  6. Lambert, A.J., Weyde, T. and Armstrong, N. (2015). Perceiving and predicting expressive rhythm with recurrent neural networks. .
  7. Cherla, S., Tran, S.N., Weyde, T. and Garcez, A.S.D. (2015). Hybrid Long- and Short-Term Models of Folk Melodies. .
  8. Cherla, S., Tran, S.N., Garcez, A.D.A. and Weyde, T. (2015). Discriminative learning and inference in the Recurrent Temporal RBM for melody modelling. .
  9. Wolff, D., MacFarlane, A. and Weyde, T. (2015). Comparative Music Similarity Modelling Using Transfer Learning Across User Groups. .
  10. Benetos, E. and Weyde, T. (2015). An Efficient Temporally-Constrained Probabilistic Model for Multiple-Instrument Music Transcription. .
  11. Kachkaev, A., Wolff, D., Barthet, M., Tidhar, D., Plumbley, M., Dykes, J. and Weyde, T. (2014). Visualising Chord Progressions in Music Collections: A Big Data Approach. Conference on Interdisciplinary Musicology – CIM14 3-6 December, Staatliches Institut für Musikforschung, Berlin, Germany.
  12. Barthet, M., Plumbley, M., Kachkaev, A., Dykes, J., Wolff, D. and Weyde, T. (2014). Big Chord Data Extraction and Mining. Conference on Interdisciplinary Musicology – CIM14 3-6 December, Staatliches Institut für Musikforschung, Berlin, Germany.
  13. Benetos, E., Jansson, A. and Weyde, T. (2014). Improving automatic music transcription through key detection. AES 53rd International Conference on Semantic Audio 27-29 January, London, UK.
  14. Benetos, E., Badeau, R., Weyde, T. and Richard, G. (2014). Template Adaptation for Improving Automatic Music Transcription. .
  15. Sigtia, S., Benetos, E., Cherla, S., Weyde, T., Garcez, A.S.D. and Dixon, S. (2014). An RNN-based Music Language Model for Improving Automatic Music Transcription. .
  16. Lambert, A., Weyde, T. and Armstrong, N. (2014). Studying the effect of metre perception on rhythm and melody modelling with LSTMs. .
  17. Lambert, A., Weyde, T. and Armstrong, N. (2014). Beyond the beat: Towards metre, rhythm and melody modelling with hybrid oscillator networks. .
  18. Wolff, D., Tidhar, D., Benetos, E., Dumon, E., Cherla, S. and Weyde, T. (2014). Incremental dataset definition for large scale musicological research. .
  19. Weyde, T., Cottrell, S., Dykes, J., Benetos, E., Wolff, D., Tidhar, D., Kachkaev, A., Plumbley, M., Dixon, S., Barthet, M., Gold, N., Abdallah, S., Alancar-Brayner, A., Mahey, M. and Tovell, A. (2014). Big data for musicology. .
  20. Benetos, E., Ewert, S. and Weyde, T. (2014). Automatic transcription of pitched and unpitched sounds from polyphonic music. .
  21. Crawford, T., Fields, B., Lewis, D., Page, K., De Valk, R. and Weyde, T. (2014). SLICKMEM - Explorations in Linked Data practice for early music. .
  22. Tran, S.N., Wolff, D., Weyde, T. and Garcez, A.D.A. (2014). Feature preprocessing with Restricted Boltzmann Machines for music similarity learning. .
  23. Wolff, D., Bellec, G., Friberg, A., MacFarlane, A. and Weyde, T. (2014). Creating audio based experiments as social Web games with the CASimIR framework. .
  24. Cherla, S., Weyde, T. and Garcez, A.S.D. (2014). Multiple Viewpiont Melodic Prediction with Fixed-Context Neural Networks. .
  25. Cherla, S., Weyde, T.E., Garcez, A. and Pearce, M. (2013). Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction. 14th International Society for Music Information Retrieval Conference 4-8 November, Curtiba, PR, Brazil.
  26. Benetos, E. and Weyde, T. (2013). Explicit duration hidden Markov models for multiple-instrument polyphonic music transcription. 14th International Society for Music Information Retrieval Conference 4-8 November, Curitiba, PR, Brazil.
  27. de Valk, R., Weyde, T. and Benetos, E. (2013). A machine learning approach to voice separation in lute tablature. 14th International Society for Music Information Retrieval Conference 4-8 November, Curitiba, PR, Brazil.
  28. Benetos, E., Cherla, S. and Weyde, T. (2013). An efficient shift-invariant model for polyphonic music transcription. 6th International Workshop on Machine Learning and Music Prague, Czech Republic.
  29. Wolff, D., Stober, S., Nürnberger, A. and Weyde, T. (2012). A Systematic Comparison of Music Similarity Adaptation Approaches. .
  30. Wolff, D. and Weyde, T. (2012). Adapting similarity on the MagnaTagATune database: effects of model and feature choices. .
  31. Weyde, T.E. and Wolff, D. (2011). On Culture-dependent Modelling of Music Similarity. 4th International Conference of Students of Systematic Musicology 5-7 October, Cologne, Germany.
  32. Weyde, T.E. and Wolff, D. (2011). Adapting Metrics for Music Similarity Using Comparative
    Ratings.
    12th International Society for Music Information October, Miami, Florida, USA.
  33. Wolff, D. and Weyde, T. (2011). Combining Sources of Description for Approximating Music Similarity Ratings. .
  34. Weyde, T.E., Wissmann, J. and Conklin, D. (2010). Representing chord sequences in OWL. Sound and Music Computing Conference 2010 July, Universidat Pompeu Fabra, Barcelona, Spain.
  35. Wissmann, J., Weyde, T.E. and Conklin, D. (2010). Representing chord sequences in OWL. Sound and Music Computing Conference 2010 21-24 June, Barcelona, Spain.
  36. Honingh, A., Weyde, T. and Conklin, D. (2009). Sequential association rules in atonal music. .
  37. Weyde, T.E., Ng, K. and Nesi, P. (2008). i-Maestro: Technology-Enhanced Learning for Music. International Computer Music Conference 24-29 August, Belfast.
  38. Weyde, T.E., Ng, K., Ong, B. and Neubarth, K. (2008). Interactive Multimedia Technology
    Enhanced Learning for Music with i-Maestro.
    World Conference on Education Multimedia, Hypermedia & Telecommunications 30 Jun 2008 – 4 Jul 2008, Vienna, Austria.
  39. Weyde, T.E., Neubarth, K., Gehrs, V., Sutton, L. and Poggio, L. (2008). The European Curriculum Challenge: a Case Study in Technology-Supported Specialised Music Education. Fourth I-MAESTRO Workshop on Technology Enhanced Music Education, co-located with the 8th International Conference new Interfaces for Musical Expression 4 June, Genova.
  40. Weyde, T., Wissmann, J. and Neubarth, K. (2007). An Experiment on the Role of Pitch Intervals in Melodic Segmentation. .
  41. Weyde, T.E., Neubarth, K. and Badii, A. (2007). Generation of Exercise Objects for Personalised Technology-Enhanced Music Learning. E-Learn Conference Quebec City, Canada.
  42. Weyde, T.E., Ng, K., Neubarth, K., Larkin, O., Koerselman, T. and Ong, B. (2007). A Systemic Approach to Music Performance Learning with Multimodal Technology. Support E-Learning Conference Quebec City, Canada.
  43. Weyde, T.E. and Wissmann, J. (2007). Experiments on the Role of Pitch Intervals in Melodic Segmentation. International Conference on Music Information Retrieval Vienna, Austria.
  44. Weyde, T.E. (2007). Automatic Semantic Annotation of Music with Harmonic Structure. 4th Sound and Music Computing Conference Lefkada, Greece.
  45. Ng, K.-.C., Weyde, T., Larkin, O., Neubarth, K., Koerselman, T. and Ong, B. (2007). 3d augmented mirror: a multimodal interface for string instrument learning and teaching with gesture support. .
  46. Wissmann, J. and Weyde, T. (2007). Using diagrams for the semantic annotation of multimedia. .
  47. Weyde, T.E. (2006). Generation of Exercises and Exercise Sequences for Technology Enhanced. 2nd International Conference on Automated Production of Cross-Media Content for Multichannel Distribution Leeds, UK.
  48. Weyde, T.E. and Enders, B. (2005). Bestimmung von Gestaltgrenzen und Gestaltähnlichkeiten für Musik-Retrieval (Determining Gestalt Boundaries and Similarities for Music Retrieval). Digital & Multimedia Music Publishing Osnabrück.
  49. Weyde, T.E. (2005). Dynamic and Interactive Visualisations of MPEG Symbolic Music Representation. 5th Open Musicnetwork Workshop University of Vienna, Austria.
  50. Weyde, T. and Datzko, C. (2005). Efficient Melody Retrieval with Motif Contour Classes. .
  51. Weyde, T.E. (2004). Application Scenarios for Music Notation in MPEG: A Music Rehearsal Companion. 3rd Interactive Musicnetwork Open Workshop .
  52. Weyde, T.E. and Wissmann, J. (2004). Dynamic Concept Maps for Music. 1st Concept Mapping Conference 2004 Unversidat Publica de Navarra, Pamplona, Spain.
  53. Weyde, T. (2004). The Influence of Pitch on Melodic Segmentation. .
  54. Weyde, T.E. (2003). Optimization of Parameter Weights in Modelling Melodic Segmentation. ESCOM 2003 Conference Hannover, Germany2.
  55. Weyde, T.E. (2003). Case Study: Leveraging Representations of Musical Structure for Music
    Software.
    Second Musicnetwork Workshop University of Leeds.
  56. Dalinghaus, K. and Weyde, T. (2003). Structure recognition on sequences with a neuro-fuzzy-system. .
  57. Weyde, T.E. (2002). Integrating Segmentation and Similarity in Melodic Analysis. International Conference on Music Perception and Cognition 2002 Causal, Sydney.
  58. Gieseking, M. and Weyde, T. (2002). Concepts of the MUSITECH infrastructure for internet-based interactive musical applications. .
  59. Noll, T., Garbers, J., Höthker, K., Spevak, C. and Weyde, T. (2002). Opuscope - Towards a Corpus-Based Music Repository. .
  60. Weyde, T.E. (2001). Grouping, similarity and the recognition of rhythmic structure. International Computer Music Conference Havana, Cuba.
  61. Weyde, T.E. and Enders, B. (1999). Das Computerkolleg Musik - Gehörbildung, eine integrierte Lernumgebung für Musikpraxis und -theorie im Hochschuleinsatz. Lernplattformen Hildesheim1.
  62. Weyde, T.E. (1999). Globale Revolution oder digitale Kontinuität (Global Revolution or Digital Continuity). KlangArt Congress Musik und Bildung .
  63. Weyde, T.E. and Wissmann, J. Semantic Interpretation of Multimedia Annotation Diagrams. 12th International Conference on Human-Computer Interaction Bejing, China.
  64. Weyde, T.E. and Neubarth, K. Using Music Processing Algorithms for Exercise Generation
    in Music E-Learning.
    2nd International Conference on Automated Production of Cross-Media Content for Multichannel Distribution .
  65. Weyde, T.E. Modelling cognitive and analytic musical structures in the Musitech
    framework.
    5th Conference on Understanding and Creating Music Seconda Universita degli Studi di Napoli. Caserta, Italy.
  66. Weyde, T.E. MPEG Symbolic Music Representation and Music Education Software. Workshop and Industrial Axmedis Conference 2005 Universita degli Studi di Firenze. Florence, Italy.
  67. Weyde, T.E. and Wissmann, J. Visualization of Musical Structure. First Conference on Interdisciplinary Musicology Graz, Austria.
  68. Weyde, T.E. and Dalinghaus, K. Recognition of Musical Rhythm Patterns Based on a
    Neuro-Fuzzy-System.
    ANNIE 2001 .
  69. Weyde, T.E. Knowledge- and Learning-Based Segmentation and Recognition of Rhythm Using Fuzzy-Prolog. 8th Journee d'Informatique Musicale Institut Internationale de Musique Electroacoustique de Bourges.
  70. Weyde, T.E. and Gieseking, M. Computer- und Internetbasiertes Musiklernen (Computer and internet based music learning). KlangArt Congress 1997 .
  71. Weyde, T.E. Recognition of rhythmic structure with a neuro-fuzzy-system. Sixth International Conference on Music Perception and Cognition Keele University, Staffordshire, UK.
  72. Weyde, T.E. Grammatikbasierte harmonische Analyse von Jazzstandards mit Computerunterstützung (Grammar Based Harmonic Analysis of Jazz Standards Aided by Computer). KlangArt Kongress 1995 Universitätsverlag Rasch, Osnabrück.

Journal Articles (14)

  1. Abdallah, S., Benetos, E., Gold, N., Hargreaves, S., Weyde, T. and Wolff, D. (2017). The digital music lab: A big data infrastructure for digital musicology. Journal on Computing and Cultural Heritage, 10(1) . doi:10.1145/2983918.
  2. Cherla, S., Tran, S.N., Weyde, T. and Garcez, A.D. (2016). Generalising the Discriminative Restricted Boltzmann Machine. .
  3. de Valk, R. and Weyde, T. (2015). Bringing 'Musicque into the tableture': machine-learning models for polyphonic transcription of 16th-century lute tablature. EARLY MUSIC, 43(4), pp. 563–+. doi:10.1093/em/cau102.
  4. Wolff, D. and Weyde, T. (2014). Learning music similarity from relative user ratings. Information Retrieval, 17(2), pp. 109–136. doi:10.1007/s10791-013-9229-0.
  5. Sigtia, S., Benetos, E., Boulanger-Lewandowski, N., Weyde, T., Garcez, A.S.D. and Dixon, S. (2014). A Hybrid Recurrent Neural Network For Music Transcription. CoRR, abs/1411.1623 .
  6. Tidhar, D., Dixon, S., Benetos, E. and Weyde, T. (2014). The temperament police. Early Music, 42(4), pp. 579–590. doi:10.1093/em/cau101.
  7. Velarde, G., Weyde, T. and Meredith, D. (2013). An approach to melodic segmentation and classification based on filtering with the Haar wavelet. Journal of New Music Research, 42(4), pp. 325–345. doi:10.1080/09298215.2013.841713.
  8. Weyde, T., Slabaugh, G., Fontaine, G. and Bederna, C. (2013). Predicting aquaplaning performance from tyre profile images with machine learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7950 LNCS, pp. 133–142. doi:10.1007/978-3-642-39094-4_16.
  9. Wolff, D. and Weyde, T. (2013). Learning music similarity from relative user ratings. Information Retrieval pp. 1–28.
  10. Wissmann, J., Weyde, T. and Conklin, D. (2010). Chord sequence patterns in OWL. Proceedings of the 7th Sound and Music Computing Conference, SMC 2010 p. 16.
  11. Weyde, T.E. and Dalinghaus, K. (2004). A Neuro-Fuzzy System for Sequence Alignment on Two
    Levels.
    Mathware and Soft ComputingDalinghaus, 9(2-3), pp. 197–210.
  12. Weyde, T.E. (2004). Modelling Rhythmic Motif Structure with Fuzzy-Logic and Machine Learning. Computing in Musicology, 13, pp. 35–50.
  13. Weyde, T. and Dalinghaus, K. (2003). Design and optimization of neuro-fuzzy-based recognition of musical rhythm patterns. International Journal of Smart Engineering System Design, 5(2), pp. 67–79. doi:10.1080/10255810305041.
  14. Weyde, T.E. and Enders, B. (1996). Automatische Rhythmuserkennung und -vergleich mit Hilfe von Fuzzy-Logik (Automatic Rhythm Recognition and Comparison Using Fuzzy Logic). Systematische Musikwissenschaft / Systematic Musicology, 4(1-2), pp. 101–113.

Report

  1. Honingh, A. and Weyde, T.E. (2008). Integrating Convexity and Compactness into the ISSM: Melodic Analysis of Music..

Software (2)

  1. Weyde, T.E. and Enders, B. (2002). Computer Courses in Music - Ear Training (English version
    of Computerkolleg Musik - Gehörbildung).
    Schott Musik International, Mainz.
  2. Weyde, T.E. and Enders, B. (1999). Computerkolleg Musik - Gehörbildung (ear training software
    ).
    Schott Musik International, Mainz.

Other Activities

Collaborations (Academic) (2)

Collaborations (Industrial) (2)

  1. Researcher of MIR Research for the World's First Computer Generated Musical project (Jul 2015 – Sep 2017)
    Sponsored by Wingspan Productions Ltd
    Other partners: Wingspan Productions for Sky Arts, Queen Mary University of London
  2. of (Mar 2012 – present)
    Other partners: Continental AG, Germany

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