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Dimos Makris
Dimos Makris
Machine Learning Researcher at Music Tribe, Sweden
Verified email at musictribe.com
Title
Cited by
Cited by
Year
Combining LSTM and feed forward neural networks for conditional rhythm composition
D Makris, M Kaliakatsos-Papakostas, I Karydis, KL Kermanidis
International conference on engineering applications of neural networks, 570-582, 2017
582017
Generating lead sheets with affect: A novel conditional seq2seq framework
D Makris, KR Agres, D Herremans
2021 international joint conference on neural networks (IJCNN), 1-8, 2021
402021
Hierarchical recurrent neural networks for conditional melody generation with long-term structure
G Zixun, D Makris, D Herremans
2021 international joint conference on neural networks (IJCNN), 1-8, 2021
362021
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses
P Chua, D Makris, D Herremans, G Roig, K Agres
arXiv preprint arXiv:2202.10453, 2022
322022
Conditional neural sequence learners for generating drums’ rhythms
D Makris, M Kaliakatsos-Papakostas, I Karydis, KL Kermanidis
Neural Computing and Applications 31 (6), 1793-1804, 2019
292019
The greek music dataset
D Makris, I Karydis, S Sioutas
Proceedings of the 16th International Conference on Engineering Applications …, 2015
232015
The greek audio dataset
D Makris, KL Kermanidis, I Karydis
IFIP International Conference on Artificial Intelligence Applications and …, 2014
172014
Learning and creating novel harmonies in diverse musical idioms: An adaptive modular melodic harmonisation system
M Kaliakatsos-Papakostas, D Makris, C Tsougras, E Cambouropoulos
Journal of Creative Music Systems 1 (1), 2016
152016
Conditional drums generation using compound word representations
D Makris, G Zixun, M Kaliakatsos-Papakostas, D Herremans
International conference on computational intelligence in music, sound, art …, 2022
142022
Automatic melodic harmonization: An overview, challenges and future directions
D Makris, I Kayrdis, S Sioutas
Trends in music information seeking, behavior, and retrieval for creativity …, 2016
142016
Could you help me to change the variables? Comparing instruction to encouragement for teaching programming
D Makris, K Euaggelopoulos, K Chorianopoulos, MN Giannakos
Proceedings of the 8th Workshop in Primary and Secondary Computing Education …, 2013
132013
Probabilistic Modular Bass Voice Leading in Melodic Harmonisation.
D Makris, MA Kaliakatsos-Papakostas, E Cambouropoulos
ISMIR, 323-329, 2015
122015
Deepdrum: An adaptive conditional neural network
D Makris, M Kaliakatsos-Papakostas, KL Kermanidis
arXiv preprint arXiv:1809.06127, 2018
112018
VISA3: Refining the voice integration/segregation algorithm
D Makris, I Karydis, E Cambouropoulos
Sound and music computing conference (SMC 2016), 2016
82016
Learning and blending harmonies in the context of a melodic harmonisation assistant
M Kaliakatsos-Papakostas, D Makris, A Zacharakis, C Tsougras, ...
IFIP international conference on artificial intelligence applications and …, 2016
62016
A probabilistic approach to determining bass voice leading in melodic harmonisation
D Makris, M Kaliakatsos-Papakostas, E Cambouropoulos
International Conference on Mathematics and Computation in Music, 128-134, 2015
62015
Generating lead sheets with affect: A novel conditional seq2seq framework. In 2021 Int. Joint Conf. on Neural Networks (IJCNN)
D Makris, KR Agres, D Herremans
IEEE, 2021
32021
A machine learning approach for midi to guitar tablature conversion
M Kaliakatsos-Papakostas, G Bastas, D Makris, D Herremans, ...
arXiv preprint arXiv:2510.10619, 2025
22025
HarmonyTok: Comparing methods for harmony tokenization for machine learning
M Kaliakatsos-Papakostas, D Makris, K Soiledis, KT Tsamis, V Katsouros, ...
Information 16 (9), 759, 2025
22025
Diffusion-Inspired Masked Language Modeling for Symbolic Harmony Generation on a Fixed Time Grid
M Kaliakatsos-Papakostas, D Makris, K Soiledis, KT Tsamis, V Katsouros, ...
Applied Sciences 15 (17), 9513, 2025
12025
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Articles 1–20