Bob L. T. Sturm

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Oded Ben-TalKingston UniversityVerificeret mail på kingston.ac.uk
Saumitra MishraExecutive Director at JP MorganVerificeret mail på jpmorgan.com
Simon DixonCentre for Digital Music, Queen Mary University of LondonVerificeret mail på qmul.ac.uk
Emmanouil BenetosQueen Mary University of LondonVerificeret mail på qmul.ac.uk
Laurent DaudetProfessor of physics, Paris Cité University / Co-CEO at LightOnVerificeret mail på espci.fr
Bhusan ChettriResearcher in AI and Machine Learning for audio and speechVerificeret mail på boracsolutions.com
Andre HolzapfelAssociate Professor, Royal Institute of TechnologyVerificeret mail på kth.se
Nick Collinsdurham universityVerificeret mail på durham.ac.uk
Mads Græsbøll ChristensenProfessor, Dept. of Electronic Systems, Aalborg UniversityVerificeret mail på es.aau.dk
Curtis RoadsProfessor Emeritus, Media Arts and Technology at UCSBVerificeret mail på mat.ucsb.edu
Corey KereliukMcGillVerificeret mail på mail.mcgill.ca
Jan LarsenTechnical University of Denmark, DTU ComputeVerificeret mail på dtu.dk
Nicolas JonasonPhD Student, KTH Royal Institute of TechnologyVerificeret mail på kth.se
Luca CasiniPostdoctoral researcher in Music and AI at KTH StockholmVerificeret mail på kth.se
Ken DéguernelCNRS - UMR 9189 CRIStALVerificeret mail på univ-lille.fr
François Pachetex Sony CSL, ex Spotify, ex LIP6, Sorbonne UniversitéVerificeret mail på ynosound.com
Daniel StollerSpotifyVerificeret mail på spotify.com
Iryna KorshunovaGhent UniversityVerificeret mail på ugent.be
João Felipe SantosNVIDIAVerificeret mail på nvidia.com
Elio QuintonUniversal Music GroupVerificeret mail på umusic.com

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Bob L. T. Sturm

Associate Professor, KTH Royal Institute of Technology, Stockholm

Verificeret mail på kth.se - Startside

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


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
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	253	2016
Local interpretable model agnostic explanations for music content analysis S Mishra, BL Sturm, S Dixon Proc. ISMIR, 2017	235	2017
Deep learning and music adversaries C Kereliuk, BL Sturm, J Larsen IEEE Transactions on Multimedia 17 (11), 2059-2071, 2015	189	2015
Classification accuracy is not enough: On the evaluation of music genre recognition systems BL Sturm Journal of Intelligent Information Systems, 2013	186	2013
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use BL Sturm arXiv preprint arXiv:1306.1461, 2013	183	2013
A survey of evaluation in music genre recognition BL Sturm Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation 8382, 29-66, 2014	177	2014
A Simple Method to Determine if a Music Information Retrieval System is a “Horse” B Sturm IEEE Trans. Multimedia 16 (6), 1636-1644, 2014	177	2014
An analysis of the GTZAN music genre dataset BL Sturm Proceedings of the second international ACM workshop on Music information …, 2012	173	2012
Comparison of orthogonal matching pursuit implementations BL Sturm, M Christensen Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th …, 2012	169	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	137	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	135	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	130	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	104	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	87	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	56	2017
Adaptive Concatenative Sound Synthesis and Its Application to Micromontage Composition BL Sturm Computer Music Journal 30 (4), 46-66, 2006	51	2006
MATConcat: An Application for Exploring Concatenative Sound Synthesis Using MATLAB. BL Sturm ICMC, 2004	47	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
Analysing the predictions of a cnn-based replay spoofing detection system B Chettri, S Mishra, BL Sturm, E Benetos 2018 IEEE spoken language technology workshop (SLT), 92-97, 2018	41	2018
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	40	2022

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