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Dr Tillman Weyde

Senior Lecturer

School of Mathematics, Computer Science and Engineering Department of Computer Science

Contact details

Address

Dr Tillman Weyde A304C, College Building
City, University of London
Northampton Square
London EC1V 0HB
United Kingdom

About

Overview

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

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

Employment

  1. Senior Lecturer, City, University of London, 2005 – present
  2. Researcher, University of Osnabrück, 2001 – 2005
  3. Visiting Lecturer in Mathematics, University of Applied Sciences at the University of Osnabrück, 2000
  4. Assistant Researcher, University of Osnabrück, 1997 – 2001
  5. Assistant Researcher & Lecturer, University of Osnabrück, 1994 – 1997

Memberships of professional organisations

  1. Member, IEEE, Jul 2013 – present
  2. Professional Member, British Computer Society (BCS), Jun 2012 – present
  3. Member, Society of Interdisciplinary Musicology, 2012 – present
  4. Member, Gesellschaft für Informatik, Jan 2003 – present
  5. Member, International Cooperative on Systematic Musicology, 1998 – present

Research

Research students

Nadine el Naggar

Attendance: Oct 2019 – Sep 2023, full-time

Thesis title: Grammar Bias in Neural Network Learning

Role: 1st Supervisor

Eric Guizzo

Attendance: Oct 2018 – Sep 2022, full-time

Thesis title: Emotion Recognition from Audio

Role: 1st Supervisor

Enrico Lopedoto

Attendance: Feb 2018 – Jan 2025, part-time

Thesis title: Controlling Extrapolation Behaviour of Neural Networks

Role: 1st Supervisor

Radha Kopparti

Attendance: Oct 2017 – Sep 2021, part-time

Thesis title: Relation Based Patterns in Neural Networks

Role: 1st Supervisor

Adriana Danilakova

Attendance: Oct 2016 – Sep 2023, part-time

Thesis title: Parsing Legal Texts with Machine Learning

Role: 1st Supervisor

Can Koluman

Attendance: Oct 2016 – present, part-time

Thesis title: Emotion Driven Machine Learning and Decision Making

Role: 1st Supervisor

Adiana Danilakova

Attendance: Sep 2016 – present, part-time

Thesis title: Image Analysis with Machine Learning and Ontological Reasoning

Role: 1st Supervisor

Gissel Velarde

Attendance: Oct 2012 – present, full-time

Thesis title: Convolutional methods for music analysis

Role: External Supervisor

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.

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.

Jens Wissmann

Attendance: Sep 2005 – Jul 2012, part-time

Thesis title: Chord Sequence patterns in OWL

Role: 1st Supervisor

Further information: Completed 2012.

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.

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.

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.

Publications

Publications by category

Chapters (5)

  1. Velarde, G., Meredith, D. and Weyde, T. (2016). A wavelet-based approach to pattern discovery in melodies. Computational Music Analysis (pp. 303–333). ISBN 978-3-319-25931-4.
  2. Weyde, T. and De Valk, R. (2016). Chord- and note-based approaches to voice separation. Computational Music Analysis (pp. 137–154). ISBN 978-3-319-25929-1.
  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). International Computer Music Association: San Francisco. ISBN 978-0-9845274-3-4.
  4. Cherla, S., Weyde, T., d’Avila Garcez, A. and Pearce, M. (2013). A distributed model for multiple-viewpoint melodic prediction. (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 (94)

  1. Guizzo, E., Weyde, T. and Leveson, J.B. (2020). Multi-Time-Scale Convolution for Emotion Recognition from Speech Audio Signals.
  2. Perez-Lapillo, J., Galkin, O. and Weyde, T. (2020). Improving Singing Voice Separation with the Wave-U-Net Using Minimum Hyperspherical Energy.
  3. Confalonieri, R., Weyde, T., Besold, T.R. and Moscoso Del Prado Martín, F. (2020). Trepan reloaded: A knowledge-driven approach to explaining black-box models.
  4. Lopedoto, E. and Weyde, T. (2020). ReLEx: Regularisation for linear extrapolation in neural networks with rectified linear units.
  5. Philps, D., Garcez, A.D. and Weyde, T. (2019). Making Good on LSTMs' Unfulfilled Promise. NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy 8-14 December, Vancouver.
  6. Kopparti, R.M. and Weyde, T. (2019). Weight Priors for Learning Identity Relations. KR2ML, NeurIPS (Neural Information Processing Systems) 8-15 December, Vancouver, Canada.
  7. Child, C., Koluman, C. and Weyde, T. (2019). Modelling Emotion Based Reward Valuation with Computational Reinforcement Learning. CogSci'19 24-27 July, Montreal, Canada.
  8. Kopparti, R.M. and Weyde, T. (2019). Modeling Interval Relations for Neural Language models. Machine Learning for Music Discovery, 36th International Conference on Machine Learning (ICML) 9-15 June, Long Beach, California, USA.
  9. Barbieri, F., Guizzo, E., Lucchesi, F., Maffei, G., Del Prado Martín, F.M. and Weyde, T. (2019). Towards a multimodal time-based empathy prediction system.
  10. Staines, T., Weyde, T. and Galkin, O. (2019). Monaural speech separation with deep learning using phase modelling and capsule networks.
  11. Jansson, A., Bittner, R.M., Ewert, S. and Weyde, T. (2019). Joint singing voice separation and F0 estimation with deep U-net architectures.
  12. Laibacher, T., Weyde, T. and Jalali, S. (2019). M2U-net: Effective and efficient retinal vessel segmentation for real-world applications.
  13. Mahdi, A., Weyde, T. and Al-Jumeily, D. (2019). Comparing unsupervised layers in neural networks for financial time series prediction.
  14. Weyde, T., Philps, D. and d'Avila Garcez, A. (2018). Continual Learning Augmented Investment Decisions. 2018 NeurIPS Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy (FEAP-AI4Fin) 2-8 December, Montreal.
  15. de Valk, R. and Weyde, T. (2018). Deep neural networks with voice entry estimation heuristics for voice separation in symbolic music representations.
  16. Jansson, A., Humphrey, E., Montecchio, N., Bittner, R., Kumar, A. and Weyde, T. (2017). Singing voice separation with deep U-Net convolutional networks.
  17. Kedyte, V., Panteli, M., Weyde, T. and Dixon, S. (2017). Geographical origin prediction of folk music recordings from the United Kingdom.
  18. Cherla, S., Tran, S.N., Garcez, A.S.D. and Weyde, T. (2017). Generalising the Discriminative Restricted Boltzmann Machines.
  19. Lambert, A.J., Weyde, T. and Armstrong, N. (2016). Adaptive Frequency Neural Networks for Dynamic Pulse and Metre Perception. 17th International Society for Music Information Retrieval Conference, ISMIR 2016 7-11 August, New York City, United States.
  20. Abdallah, S., Benetos, E., Gold, N., Hargreaves, S., Weyde, T. and Wolff, D. (2016). Digital music lab: A framework for analysing big music data.
  21. 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.
  22. 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.
  23. Colton, S., Llano, M.T., Hepworth, R., Charnley, J., Gale, C.V., Baron, A. … Lloyd, J.R. (2016). The beyond the Fence musical and computer says show documentary.
  24. Velarde, G., Weyde, T., Chacón, C.C., Meredith, D. and Grachten, M. (2016). Composer recognition based on 2D-filtered piano-rolls.
  25. 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.
  26. Sigtia, S., Benetos, E., Boulanger-Lewandowski, N., Weyde, T., Garcez, A.S.D., Dixon, S. … IEEE, (2015). A HYBRID RECURRENT NEURAL NETWORK FOR MUSIC TRANSCRIPTION.
  27. Wolff, D., MacFarlane, A. and Weyde, T. (2015). Comparative music similarity modelling using transfer learning across user groups.
  28. 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.
  29. Lambert, A.J., Weyde, T. and Armstrong, N. (2015). Perceiving and predicting expressive rhythm with recurrent neural networks.
  30. Cherla, S., Tran, S.N., Weyde, T. and d’Avila Garcez, A. (2015). Hybrid long- and short-term models of folk melodies.
  31. Benetos, E. and Weyde, T. (2015). An efficient temporally-constrained probabilistic model for multiple-instrument music transcription.
  32. 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.
  33. Kachkaev, A., Wolff, D., Barthet, M., Tidhar, D., Plumbley, M., Dykes, J. … 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.
  34. 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.
  35. Benetos, E., Ewert, S. and Weyde, T. (2014). Automatic transcription of pitched and unpitched sounds from polyphonic music.
  36. Wolff, D., Bellec, G., Friberg, A., MacFarlane, A. and Weyde, T. (2014). Creating audio based experiments as social Web games with the CASimIR framework.
  37. Crawford, T., Fields, B., Lewis, D., Page, K., De Valk, R. and Weyde, T. (2014). SLICKMEM - Explorations in Linked Data practice for early music.
  38. Tran, S.N., Wolff, D., Weyde, T. and Garcez, A.D.A. (2014). Feature preprocessing with Restricted Boltzmann Machines for music similarity learning.
  39. Wolff, D., Tidhar, D., Benetos, E., Dumon, E., Cherla, S. and Weyde, T. (2014). Incremental dataset definition for large scale musicological research.
  40. Weyde, T., Cottrell, S., Dykes, J., Benetos, E., Wolff, D., Tidhar, D. … Tovell, A. (2014). Big data for musicology.
  41. Lambert, A., Weyde, T. and Armstrong, N. (2014). Beyond the beat: Towards metre, rhythm and melody modelling with hybrid oscillator networks.
  42. Lambert, A., Weyde, T. and Armstrong, N. (2014). Studying the effect of metre perception on rhythm and melody modelling with LSTMs.
  43. Benetos, E., Badeau, R., Weyde, T. and Richard, G. (2014). Template adaptation for improving automatic music transcription.
  44. Sigtia, S., Benetos, E., Cherla, S., Weyde, T., d’Avila Garcez, A.S. and Dixon, S. (2014). An RNN-based music language model for improving automatic music transcription.
  45. Cherla, S., Weyde, T. and d’Avila Garcez, A. (2014). Multiple viewpoint melodic prediction with fixed-context neural networks.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. Wolff, D. and Weyde, T. (2012). Adapting similarity on the MagnaTagATune database: effects of model and feature choices.
  51. Wolff, D., Stober, S., Nürnberger, A. and Weyde, T. (2012). A Systematic Comparison of Music Similarity Adaptation Approaches.
  52. 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.
  53. 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.
  54. Wolff, D. and Weyde, T. (2011). Combining Sources of Description for Approximating Music Similarity Ratings.
  55. 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.
  56. 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.
  57. Honingh, A., Weyde, T. and Conklin, D. (2009). Sequential association rules in atonal music.
  58. Weyde, T.E., Ng, K. and Nesi, P. (2008). i-Maestro: Technology-Enhanced Learning for Music. International Computer Music Conference 24-29 August, Belfast.
  59. 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.
  60. 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.
  61. 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.
  62. Wissmann, J. and Weyde, T. (2007). Using diagrams for the semantic annotation of multimedia.
  63. Weyde, T.E. (2007). Automatic Semantic Annotation of Music with Harmonic Structure. 4th Sound and Music Computing Conference Lefkada, Greece.
  64. 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.
  65. 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.
  66. 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.
  67. Weyde, T., Wissmann, J. and Neubarth, K. (2007). An Experiment on the Role of Pitch Intervals in Melodic Segmentation.
  68. 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.
  69. Weyde, T. and Datzko, C. (2005). Efficient Melody Retrieval with Motif Contour Classes.
  70. Weyde, T.E. (2005). Dynamic and Interactive Visualisations of MPEG Symbolic Music Representation. 5th Open Musicnetwork Workshop University of Vienna, Austria.
  71. 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.
  72. Weyde, T. (2004). The Influence of Pitch on Melodic Segmentation.
  73. Weyde, T.E. and Wissmann, J. (2004). Dynamic Concept Maps for Music. 1st Concept Mapping Conference 2004 Unversidat Publica de Navarra, Pamplona, Spain.
  74. Weyde, T.E. (2004). Application Scenarios for Music Notation in MPEG: A Music Rehearsal Companion. 3rd Interactive Musicnetwork Open Workshop.
  75. Dalinghaus, K. and Weyde, T. (2003). Structure recognition on sequences with a neuro-fuzzy-system.
  76. Weyde, T.E. (2003). Case Study: Leveraging Representations of Musical Structure for Music
    Software.     Second Musicnetwork Workshop University of Leeds.
  77. Weyde, T.E. (2003). Optimization of Parameter Weights in Modelling Melodic Segmentation. ESCOM 2003 Conference Hannover, Germany2.
  78. Gieseking, M. and Weyde, T. (2002). Concepts of the MUSITECH infrastructure for internet-based interactive musical applications.
  79. Noll, T., Garbers, J., Höthker, K., Spevak, C. and Weyde, T. (2002). Opuscope - Towards a Corpus-Based Music Repository.
  80. Weyde, T.E. (2002). Integrating Segmentation and Similarity in Melodic Analysis. International Conference on Music Perception and Cognition 2002 Causal, Sydney.
  81. Weyde, T.E. (2001). Grouping, similarity and the recognition of rhythmic structure. International Computer Music Conference Havana, Cuba.
  82. Weyde, T.E. (1999). Globale Revolution oder digitale Kontinuität (Global Revolution or Digital Continuity). KlangArt Congress Musik und Bildung.
  83. Weyde, T.E. and Enders, B. (1999). Das Computerkolleg Musik - Gehörbildung, eine integrierte Lernumgebung für Musikpraxis und -theorie im Hochschuleinsatz. Lernplattformen Hildesheim1.
  84. 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.
  85. Weyde, T.E. Recognition of rhythmic structure with a neuro-fuzzy-system. Sixth International Conference on Music Perception and Cognition Keele University, Staffordshire, UK.
  86. Weyde, T.E. and Gieseking, M. Computer- und Internetbasiertes Musiklernen (Computer and internet based music learning). KlangArt Congress 1997.
  87. 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.
  88. Weyde, T.E. and Dalinghaus, K. Recognition of Musical Rhythm Patterns Based on a
    Neuro-Fuzzy-System.     ANNIE 2001.
  89. Weyde, T.E. and Wissmann, J. Visualization of Musical Structure. First Conference on Interdisciplinary Musicology Graz, Austria.
  90. Weyde, T.E. MPEG Symbolic Music Representation and Music Education Software. Workshop and Industrial Axmedis Conference 2005 Universita degli Studi di Firenze. Florence, Italy.
  91. 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.
  92. 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.
  93. Weyde, T.E. and Wissmann, J. Semantic Interpretation of Multimedia Annotation Diagrams. 12th International Conference on Human-Computer Interaction Bejing, China.
  94. Philps, D., Weyde, T., Garcez, A.D. and Batchelor, R. Continual Learning Augmented Investment Decisions.

Journal articles (18)

  1. Tran, S.N., Garcez, A.D.A., Weyde, T., Yin, J., Zhang, Q. and Karunanithi, M. (2020). Sequence Classification Restricted Boltzmann Machines with Gated Units. IEEE Transactions on Neural Networks and Learning Systems, 31(11), pp. 4806–4815. doi:10.1109/TNNLS.2019.2958103.
  2. Weyde, T. and Kopparti, R.M. (2019). Modelling identity rules with neural networks. Journal of Applied Logics, 6(4), pp. 745–769.
  3. Velarde, G., Cancino Chacón, C., Meredith, D., Weyde, T. and Grachten, M. (2018). Convolution-based classification of audio and symbolic representations of music. Journal of New Music Research, 47(3), pp. 191–205. doi:10.1080/09298215.2018.1458885.
  4. Mahdi, A., Weyde, T. and Al-Jumeily, D. (2018). The FL-SMIA Network: A Novel Architecture for Time Series Prediction. 2017 10TH INTERNATIONAL CONFERENCE ON DEVELOPMENTS IN ESYSTEMS ENGINEERING (DESE 2017) pp. 31–36. doi:10.1109/DeSE.2017.42.
  5. Elmsley, A., Weyde, T. and Armstrong, N. (2017). Generating Time: Rhythmic Perception, Prediction and Production with Recurrent Neural Networks. Journal of Creative Music Systems, 1(2). doi:10.5920/JCMS.2017.04.

    [publisher’s website]

  6. Cherla, S., Tran, S.N., D’Avila Garcez, A. and Weyde, T. (2017). Generalising the discriminative restricted boltzmann machines. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10614 LNCS, pp. 111–119. doi:10.1007/978-3-319-68612-7_13.
  7. 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.
  8. Tran, S.N., Cherla, S., Garcez, A.S.D. and Weyde, T. (2017). The Recurrent Temporal Discriminative Restricted Boltzmann Machines. CoRR, abs/1710.02245.
  9. 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. 563–576. doi:10.1093/em/cau102.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. Weyde, T.E. (2004). Modelling Rhythmic Motif Structure with Fuzzy-Logic and Machine Learning. Computing in Musicology, 13, pp. 35–50.
  16. 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.
  17. 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.
  18. 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.

Professional activities

Collaborations (academic) (2)

  1. of Development of Games With A Purpose for Online Data Collection project (Apr 2013 – present)
    Other partners: Anders Friberg, Anders Elowson
  2. of Wavelet-based analysis of symbolic music representations project (Oct 2012 – present)
    Other partners: Gissel Velarde, David Meredith

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

Consultancy (3)

  1. University of Oxford / Eduserv (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
  2. 'NEUMES Digital Encoding of Medieval Chant' at Harvard (2003 – 2004)
    Consultant to the Harvard based project 'NEUMES Digital Encoding of Medieval Chant' funded by the Andrew E. Mellon foundation.
  3. Independent consultant on multimedia information & education systems (1998 – 2001)