- Our article "The temperament police" was the most read article in Early Music in 2014 (Top Five OUP Music Journal Articles of 2014) and available freely here.
- I participated in the Music Similarity: Concepts, Cognition and Computation from 19-23 Jan 2015.
- Our workgroup, especially Andy Lambert, worked with a robot orchestra in the Royal Institution Christmas Lectures 2014
- We started the ASymMuS project in September 2014.
- We started the data analysis project funded by Innovate UK (formerly Technology Strategy Board) and EPSRC on Advancing Consumer Protection Through Machine Learning: Reducing Harm in Gambling
Short Research BioI am a Senior Lecturer in the Department of Computer Science, head of the Music Informatics Research Group and a member of the Machine Learning Group. I work on Machine Learning methods for data analysis with applications in audio, music, health and education. Before I joined City I was a researcher and coordinator of the MUSITECH project at the Research Department of Music and Media Technology at the University of Osnabrück. I hold degrees in Computer Science, Music, and Mathematics and obtained my PhD in Music Technology on the topic of on combining knowledge and machine learning in the automatic analysis of rhythms. I am is an associated member of the Institute of Cognitive Science and the Research Department of Music and Media Technology of the University of Osnabrück. I am 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 I am a member of the MPEG Ad-Hoc-Group on Symbolic Music Representation (SMR), working on the integration of SMR into MPEG-4. I was the principal investigator at City in the music e-learning project i-Maestro which was supported by the European Commission (FP6). I currently work on methods for automatic music analysis and transcription, audio-based similarity and recommendation, Semantic Web representations for music and general applications of audio processing and machine learning in industry and science. I have received funding from the AHRC for the Digital Transformations Project Digital Music Lab - Analysing Big Music Data (DML), a joint project with the British Library, Queen Mary University of London, University College London, and I Like Music. More recently we started the AHRC Amplification Project on An Integrated Audio-Symbolic Model of Music Similarity where we apply the results from the DML. I am also engaged as a co-investigator in a project funded by Innovate UK (formerly Technology Strategy Board) and EPSRC on Advancing Consumer Protection Through Machine Learning: Reducing Harm in Gambling.
Here is a link to my standard staff homepage.
Try our game: Spot the Odd Song Out!Play our Game With a Purpose on the Web or on Facebook
- Sigtia, S., Benetos, E., Boulanger-Lewandowski, N., Weyde, T., d’Avila Garcez, A., and Dixon, S. (2015). A hybrid recurrent neural network for music transcription. IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr. 2015, accepted.
- De Valk, R., Weyde, T. (2015). Bringing ‘Musicque into the tableture’: Machine learning models for polyphonic transcription of sixteenth-century lute tablature. Early Music, accepted.
- Kachkaev, A., Wolff, D., Barthet, M., Plumbley, M., Dykes, J., and Weyde, T. (2014). Visualising Chord Progressions in Music Collections: A Big Data Approach. In: Conference on Interdisciplinary Musicology, Dec. 2014.
- Barthet, M., Plumbley, M., Kachkaev, A., Dykes, J., Wolff, D., and Weyde, T. (2014). Big Chord Data Extraction and Mining. In: Conference on Interdisciplinary Musicology, Dec. 2014.
- Tidhar, D., Dixon, S., Benetos, E., and Weyde, T. (2014). The Temperament Police. Early Music, 42(4):579-590, Nov. 2014.
- Wolff, D., Weyde, T. (2013). Learning music similarity from relative user ratings. In: Information Retrieval, July 2013. ISSN 1386-4564. DOI 10.1007/s10791-013-9229-0.
- Weyde, T., Slabaugh, G., Fontaine, G., and Bederna, C. (2013). Predicting Aquaplaning Performance from Tyre Profile Images with Machine Learning. In: Image Analysis and Recognition. Lecture Notes in Computer Science, Volume 7950, 2013, pp 133-142. Preprint
- Wissmann J., Weyde, T., Conklin, D. (2010). Representing chord sequences in OWL. In: Proceedings of the Sound and Music Computing Conference 2010. Universidat Pompeu Fabra, Barcelona, Spain, July 2010.
- Honingh, A., Weyde, T. and Conklin, D. (2009). Sequential Association Rules in Atonal Music. In: Proceedings of the Second International Conference on Mathematics and Computation in Music. Yale University, New Haven, Connecticut, USA, 19 - 22 June 2009.
Office Hours for Students
My drop-in hour during term time is:
- Tuesdays 17:00-18:00
- Wednesdays 11:00-12:00
For meetings outside that time, pelase send me an e-mail.