Bob L. T. Sturm

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Oded Ben-TalKingston UniversityVerified email at kingston.ac.uk
Emmanouil BenetosQueen Mary University of London / The Alan Turing InstituteVerified email at qmul.ac.uk
Saumitra MishraJP MorganVerified email at jpmorgan.com
Laurent DaudetProfessor of physics, Paris Cité University / Co-CEO at LightOnVerified email at espci.fr
Simon DixonCentre for Digital Music, Queen Mary University of LondonVerified email at qmul.ac.uk
Bhusan ChettriResearcher in AI and Machine Learning for audio and speechVerified email at boracsolutions.com
Mads Græsbøll ChristensenProfessor, Dept. of Electronic Systems, Aalborg UniversityVerified email at es.aau.dk
Corey KereliukMcGillVerified email at mail.mcgill.ca
Nick Collinsdurham universityVerified email at durham.ac.uk
Jan LarsenTechnical University of Denmark, DTU ComputeVerified email at dtu.dk
François PachetSpotify Creator Technology Research Lab, directorVerified email at spotify.com
Andre HolzapfelAssociate Professor, Royal Institute of TechnologyVerified email at kth.se
Jerry GibsonProfessor of Electrical and Computer Engineering, UCSBVerified email at ece.ucsb.edu
Iryna KorshunovaGhent UniversityVerified email at ugent.be
João Felipe SantosNVIDIAVerified email at nvidia.com
Daniel StollerSpotifyVerified email at spotify.com
Elio QuintonUniversal Music GroupVerified email at umusic.com
Gaëtan HadjeresResearch Scientist at Sony AIVerified email at sony.com
Emmanuel DerutyMusic team manager, Sony CSL ParisVerified email at sony.com
Dorien HerremansSingapore University of Technology and DesignVerified email at sutd.edu.sg

Bob L. T. Sturm

Associate Professor, KTH Royal Institute of Technology, Stockholm

Verified email at kth.se - Homepage

audio and music signal processing music information retrieval music generation evaluation sparse approximation


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Music transcription modelling and composition using deep learning BL Sturm, JF Santos, O Ben-Tal, I Korshunova Proc. 1st Conference on Computer Simulation of Musical Creativity, 2016	183	2016
A survey of evaluation in music genre recognition BL Sturm Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation 8382, 29-66, 2014	164	2014
Classification accuracy is not enough: On the evaluation of music genre recognition systems BL Sturm Journal of Intelligent Information Systems, 2013	162	2013
Deep learning and music adversaries C Kereliuk, BL Sturm, J Larsen IEEE Transactions on Multimedia 17 (11), 2059-2071, 2015	160	2015
A Simple Method to Determine if a Music Information Retrieval System is a “Horse” B Sturm IEEE Trans. Multimedia 16 (6), 1636-1644, 2014	155	2014
An analysis of the GTZAN music genre dataset BL Sturm Proceedings of the second international ACM workshop on Music information …, 2012	145	2012
Comparison of orthogonal matching pursuit implementations BL Sturm, M Christensen Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th …, 2012	138	2012
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use BL Sturm arXiv preprint arXiv:1306.1461, 2013	125	2013
The state of the art ten years after a state of the art: Future research in music information retrieval BL Sturm Journal of new music research 43 (2), 147-172, 2014	111	2014
Local interpretable model agnostic explanations for music content analysis S Mishra, BL Sturm, S Dixon Proc. ISMIR, 2017	103	2017
Machine learning research that matters for music creation: A case study BL Sturm, O Ben-Tal, Ú Monaghan, N Collins, D Herremans, E Chew, ... Journal of New Music Research 48 (1), 36-55, 2019	89	2019
Ensemble models for spoofing detection in automatic speaker verification B Chettri, D Stoller, V Morfi, MAM Ramírez, E Benetos, BL Sturm arXiv preprint arXiv:1904.04589, 2019	66	2019
Ethical dimensions of music information retrieval technology A Holzapfel, B Sturm, M Coeckelbergh Transactions of the International Society for Music Information Retrieval 1 …, 2018	53	2018
Artificial intelligence and music: open questions of copyright law and engineering praxis BLT Sturm, M Iglesias, O Ben-Tal, M Miron, E Gómez Arts 8 (3), 115, 2019	51	2019
Adaptive Concatenative Sound Synthesis and Its Application to Micromontage Compositior BL Sturm Computer Music Journal 30 (4), 46-66, 2006	46	2006
Taking the models back to music practice: Evaluating generative transcription models built using deep learning BL Sturm, O Ben-Tal Journal of Creative Music Systems 2, 32-60, 2017	44	2017
MATConcat: An Application for Exploring Concatenative Sound Synthesis Using MATLAB. BL Sturm ICMC, 2004	42	2004
Musical instrument identification using multiscale mel-frequency cepstral coefficients BL Sturm, M Morvidone, L Daudet Signal Processing Conference (EUSIPCO), 2010 Proceedings of the 18th European, 2010	40	2010
A multimodal system for gesture recognition in interactive music performance D Overholt, J Thompson, L Putnam, B Bell, J Kleban, B Sturm, ... Computer Music Journal 33 (4), 69-82, 2009	38	2009
Signals and systems using MATLAB: An integrated suite of applications for exploring and teaching media signal processing BL Sturm, JD Gibson Proceedings Frontiers in Education 35th Annual Conference, F2E-21, 2005	36	2005

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