Bob L. T. Sturm

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Oded Ben-TalKingston UniversityVerified email at kingston.ac.uk
Saumitra MishraExecutive Director at JP MorganVerified email at jpmorgan.com
Emmanouil BenetosQueen Mary University of LondonVerified email at qmul.ac.uk
Simon DixonCentre for Digital Music, Queen Mary University of LondonVerified email at qmul.ac.uk
Laurent DaudetProfessor of physics, Paris Cité University / Co-CEO at LightOnVerified email at espci.fr
Bhusan ChettriResearcher in AI and Machine Learning for audio and speechVerified email at boracsolutions.com
Nick Collinsdurham universityVerified email at durham.ac.uk
Mads Græsbøll ChristensenProfessor, Dept. of Electronic Systems, Aalborg UniversityVerified email at es.aau.dk
Andre HolzapfelAssociate Professor, Royal Institute of TechnologyVerified email at kth.se
Curtis RoadsProfessor Emeritus, Media Arts and Technology at UCSBVerified email at mat.ucsb.edu
Corey KereliukMcGillVerified email at mail.mcgill.ca
Jan LarsenTechnical University of Denmark, DTU ComputeVerified email at dtu.dk
Nicolas JonasonPhD Student, KTH Royal Institute of TechnologyVerified email at kth.se
Luca CasiniPostdoctoral researcher in Music and AI at KTH StockholmVerified email at kth.se
Daniel StollerSpotifyVerified email at spotify.com
Iryna KorshunovaGhent UniversityVerified email at ugent.be
João Felipe SantosNVIDIAVerified email at nvidia.com
Ken DéguernelCNRS - UMR 9189 CRIStALVerified email at univ-lille.fr
Jerry GibsonProfessor of Electrical and Computer Engineering, UCSBVerified email at ece.ucsb.edu
Elio QuintonUniversal Music GroupVerified email at umusic.com

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	234	2016
Local interpretable model agnostic explanations for music content analysis S Mishra, BL Sturm, S Dixon Proc. ISMIR, 2017	206	2017
Deep learning and music adversaries C Kereliuk, BL Sturm, J Larsen IEEE Transactions on Multimedia 17 (11), 2059-2071, 2015	181	2015
A survey of evaluation in music genre recognition BL Sturm Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation 8382, 29-66, 2014	180	2014
Classification accuracy is not enough: On the evaluation of music genre recognition systems BL Sturm Journal of Intelligent Information Systems, 2013	178	2013
A Simple Method to Determine if a Music Information Retrieval System is a “Horse” B Sturm IEEE Trans. Multimedia 16 (6), 1636-1644, 2014	172	2014
Comparison of orthogonal matching pursuit implementations BL Sturm, M Christensen Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th …, 2012	166	2012
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use BL Sturm arXiv preprint arXiv:1306.1461, 2013	165	2013
An analysis of the GTZAN music genre dataset BL Sturm Proceedings of the second international ACM workshop on Music information …, 2012	165	2012
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	129	2014
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	120	2019
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	116	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	100	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	83	2018
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	53	2017
Adaptive Concatenative Sound Synthesis and Its Application to Micromontage Composition BL Sturm Computer Music Journal 30 (4), 46-66, 2006	49	2006
MATConcat: An Application for Exploring Concatenative Sound Synthesis Using MATLAB. BL Sturm ICMC, 2004	45	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	44	2010
The beyond the fence musical and computer says show documentary S Colton, MT Llano, R Hepworth, J Charnley, CV Gale, A Baron, F Pachet, ... arXiv preprint arXiv:2206.03224, 2022	39	2022
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

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