Tillman Weyde

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Emmanouil BenetosQueen Mary University of London / The Alan Turing InstituteDirección de correo verificada de qmul.ac.uk
Daniel WolffIRCAM Paris / FreelancerDirección de correo verificada de city.ac.uk
Artur d'Avila GarcezCity, University of LondonDirección de correo verificada de city.ac.uk
Andreas JanssonCity University, LondonDirección de correo verificada de city.ac.uk
Simon DixonCentre for Digital Music, Queen Mary University of LondonDirección de correo verificada de qmul.ac.uk
Kia NgICSRiM - University of LeedsDirección de correo verificada de leeds.ac.uk
David MeredithAssociate Professor, Aalborg UniversityDirección de correo verificada de create.aau.dk
Nicolas GoldAssociate Professor in Computer Science, University College LondonDirección de correo verificada de ucl.ac.uk
Samer AbdallahJukedeck LimitedDirección de correo verificada de jukedeck.com
Jason DykesCity, University of LondonDirección de correo verificada de city.ac.uk
Alexander KachkaevPhD in GIS, City University LondonDirección de correo verificada de city.ac.uk
Mark D. PlumbleyCentre for Vision, Speech and Signal Processing (CVSSP), University of SurreyDirección de correo verificada de surrey.ac.uk
Siddharth SigtiaAppleDirección de correo verificada de apple.com
Greg SlabaughProfessor, and Director of DERI, Queen Mary University of LondonDirección de correo verificada de qmul.ac.uk
Paolo NesiUniversity of Florence, DINFO, DISIT LabDirección de correo verificada de unifi.it
Nicolas Boulanger-LewandowskiGoogleDirección de correo verificada de google.com
Marcus PearceSenior Lecturer in Sound and Music Processing, Queen Mary University of LondonDirección de correo verificada de qmul.ac.uk
Andreas NürnbergerProfessor für Data and Knowledge Engineering, Otto-von-Guericke-Universität MagdeburgDirección de correo verificada de ovgu.de
Sebastian StoberArtificial Intelligence Lab @ Otto von Guericke University MagdeburgDirección de correo verificada de ovgu.de
Anders FribergKTH Royal Institute of TechnologyDirección de correo verificada de kth.se

Tillman Weyde

Senior Lecturer in Computer Science, City University London

Dirección de correo verificada de city.ac.uk - Página principal

Machine Learning Time Series Music Informatics Deep Learning Architectures Artificial Intelligence


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Singing voice separation with deep U-Net convolutional networks A Jansson, E Humphrey, N Montecchio, R Bittner, A Kumar, T Weyde International Society for Music Information Retrieval, Suzhou, China …, 2017	242	2017
An efficient temporally-constrained probabilistic model for multiple-instrument music transcription E Benetos, T Weyde International Society for Music Information Retrieval Conference (ISMIR …, 2015	70	2015
3D augmented mirror: a multimodal interface for string instrument learning and teaching with gesture support KC Ng, T Weyde, O Larkin, K Neubarth, T Koerselman, B Ong Proceedings of the 9th international conference on Multimodal interfaces …, 2007	61	2007
An efficient shift-invariant model for polyphonic music transcription E Benetos, S Cherla, T Weyde	46	2013
A hybrid recurrent neural network for music transcription S Sigtia, E Benetos, N Boulanger-Lewandowski, T Weyde, ASA Garcez, ... 2015 IEEE international conference on acoustics, speech and signal …, 2015	40	2015
RNN-based music language models for improving automatic music transcription S Sigtia, E Benetos, S Cherla, T Weyde, A Garcez, S Dixon	38	2014
Improved speech enhancement with the wave-u-net C Macartney, T Weyde arXiv preprint arXiv:1811.11307, 2018	36	2018
An approach to melodic segmentation and classification based on filtering with the Haar-wavelet G Velarde, T Weyde, D Meredith Journal of New Music Research 42 (4), 325-345, 2013	36	2013
Automatic transcription of pitched and unpitched sounds from polyphonic music E Benetos, S Ewert, T Weyde 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014	31	2014
M2u-net: Effective and efficient retinal vessel segmentation for real-world applications T Laibacher, T Weyde, S Jalali Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	25*	2019
The beyond the fence musical and computer says show documentary S Colton, T Llano, R Hepworth, J Charnley, C Gale, A Baron, F Pachet, ...	22	2016
i-Maestro: Technology-Enhanced Learning and Teaching for Music. K Ng, P Nesi NIME, 225-228, 2008	20	2008
The digital music lab: A big data infrastructure for digital musicology S Abdallah, E Benetos, N Gold, S Hargreaves, T Weyde, D Wolff Journal on Computing and Cultural Heritage (JOCCH) 10 (1), 1-21, 2017	19	2017
Learning music similarity from relative user ratings D Wolff, T Weyde Information retrieval 17 (2), 109-136, 2014	19	2014
Adapting Metrics for Music Similarity Using Comparative Ratings. D Wolff, T Weyde ISMIR, 73-78, 2011	19	2011
Explicit duration hidden markov models for multiple-instrument polyphonic music transcription E Benetos, T Weyde	18	2013
Template adaptation for improving automatic music transcription E Benetos, R Badeau, T Weyde, G Richard	16	2014
Integrating segmentation and similarity in melodic analysis T Weyde Proceedings of the International Conference on Music Perception and …, 2002	16	2002
A Distributed Model For Multiple-Viewpoint Melodic Prediction. S Cherla, T Weyde, A Garcez, M Pearce ISMIR, 15-20, 2013	15	2013
A systematic comparison of music similarity adaptation approaches D Wolff, S Stober, A Nürnberger, T Weyde ISMIR, 103-108, 2012	15	2012

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