| Local interpretable model agnostic explanations for music content analysis S Mishra, BL Sturm, S Dixon Proc. ISMIR, 2017 | 310 | 2017 |
| 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 | 283 | 2016 |
| The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use BL Sturm arXiv preprint arXiv:1306.1461, 2013 | 227 | 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 | 202 | 2014 |
| Deep learning and music adversaries C Kereliuk, BL Sturm, J Larsen IEEE Transactions on Multimedia 17 (11), 2059-2071, 2015 | 197 | 2015 |
| Classification accuracy is not enough: On the evaluation of music genre recognition systems BL Sturm Journal of Intelligent Information Systems, 2013 | 196 | 2013 |
| An analysis of the GTZAN music genre dataset BL Sturm Proceedings of the second international ACM workshop on Music information …, 2012 | 187 | 2012 |
| 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 | 181 | 2019 |
| Comparison of orthogonal matching pursuit implementations BL Sturm, M Christensen Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th …, 2012 | 179 | 2012 |
| 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 | 177 | 2019 |
| A survey of evaluation in music genre recognition BL Sturm Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation 8382, 29-66, 2014 | 177 | 2014 |
| 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 | 152 | 2014 |
| Ethical dimensions of music information retrieval technology A Holzapfel, B Sturm, M Coeckelbergh Transactions of the International Society for Music Information Retrieval 1 …, 2018 | 125 | 2018 |
| 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 | 119 | 2019 |
| 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 | 67 | 2017 |
| How Music AI Is Useful: Engagements with Composers, Performers and Audiences O Ben-Tal, MT Harris, BLT Sturm Leonardo 54 (5), 510-516, 2021 | 56 | 2021 |
| Adaptive Concatenative Sound Synthesis and Its Application to Micromontage Composition BL Sturm Computer Music Journal 30 (4), 46-66, 2006 | 55 | 2006 |
| De-centering the West: East Asian Philosophies and the Ethics of Applying Artificial Intelligence to Music. RS Huang, BLT Sturm, A Holzapfel ISMIR, 301-309, 2021 | 50 | 2021 |
| 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 | 50 | 2018 |
| MATConcat: An Application for Exploring Concatenative Sound Synthesis Using MATLAB. BL Sturm ICMC, 2004 | 47 | 2004 |
Oded Ben-TalKingston UniversityVerified email at kingston.ac.uk
