| A functional taxonomy of music generation systems D Herremans, CH Chuan, E Chew ACM Computing Surveys (CSUR) 50 (5), 1-30, 2017 | 244 | 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 | 136 | 2019 |
| Dance Hit Song Prediction D Herremans, D Martens, K Sörensen Journal of New Music Research, Special Issue on Music and Machine Learning …, 2014 | 118 | 2014 |
| MorpheuS: generating structured music with constrained patterns and tension D Herremans, E Chew IEEE Transactions on Affective Computing 10 (4), 510-523, 2017 | 117 | 2017 |
| Hear: Holistic evaluation of audio representations J Turian, J Shier, HR Khan, B Raj, BW Schuller, CJ Steinmetz, C Malloy, ... NeurIPS 2021 Competitions and Demonstrations Track, 125-145, 2022 | 111 | 2022 |
| nnAudio: An on-the-fly gpu audio to spectrogram conversion toolbox using 1d convolutional neural networks KW Cheuk, H Anderson, K Agres, D Herremans IEEE Access 8, 161981-162003, 2020 | 111 | 2020 |
| Music, computing, and health: a roadmap for the current and future roles of music technology for health care and well-being KR Agres, RS Schaefer, A Volk, S Van Hooren, A Holzapfel, S Dalla Bella, ... Music & Science 4, 2059204321997709, 2021 | 93 | 2021 |
| Toward robust audio spoofing detection: A detailed comparison of traditional and learned features BT Balamurali, KE Lin, S Lui, JM Chen, D Herremans IEEE Access 7, 84229-84241, 2019 | 89 | 2019 |
| Music fadernets: Controllable music generation based on high-level features via low-level feature modelling HH Tan, D Herremans International Society of Music Information Retrieval (ISMIR), 2020 | 85 | 2020 |
| Tension ribbons: Quantifying and visualising tonal tension. D Herremans, E Chew Second International Conference on Technologies for Music Notation and …, 2016 | 76 | 2016 |
| Evaluating the effectiveness of an augmented reality game promoting environmental action K Wang, ZD Tekler, L Cheah, D Herremans, L Blessing Sustainability 13 (24), 13912, 2021 | 62 | 2021 |
| Learning disentangled representations of timbre and pitch for musical instrument sounds using gaussian mixture variational autoencoders YJ Luo, K Agres, D Herremans 20th Conference of the International Society for Music Information Retrieval …, 2019 | 62 | 2019 |
| Generating structured music for bagana using quality metrics based on Markov models D Herremans, S Weisser, K Sörensen, D Conklin Expert Systems with Applications 42 (21), 7424–7435, 2015 | 62 | 2015 |
| Composing Fifth Species Counterpoint Music With A Variable Neighborhood Search Algorithm D Herremans, K Sörensen Expert Systems with Applications 40 (16), 6427--6437, 2013 | 62 | 2013 |
| Modeling Musical Context with Word2vec D Herremans, CH Chuan First International Workshop on Deep Learning and Music, Anchorage, US 1 (1 …, 2017 | 61 | 2017 |
| From context to concept: exploring semantic relationships in music with word2vec CH Chuan, K Agres, D Herremans Neural Computing and Applications 32, 1023-1036, 2020 | 60 | 2020 |
| Mustango: Toward controllable text-to-music generation J Melechovsky, Z Guo, D Ghosal, N Majumder, D Herremans, S Poria Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 58 | 2024 |
| Singing voice conversion with disentangled representations of singer and vocal technique using variational autoencoders YJ Luo, CC Hsu, K Agres, D Herremans ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 56 | 2020 |
| Composing first species counterpoint with a variable neighbourhood search algorithm D Herremans, K Sörensen Journal of Mathematics and the Arts 6 (4), 169-189, 2012 | 56 | 2012 |
| Modeling Temporal Tonal Relations in Polyphonic Music Through Deep Networks with a Novel Image-Based Representation CH Chuan, D Herremans AAAI, 2018 | 48 | 2018 |
Kat AgresNational University of Singapore在 nus.edu.sg 的電子郵件地址已通過驗證
