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pro vyhledávání: '"Garoufis, Christos"'
In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks. To this end, we first pre-train U-Net networks under various music source separa
Externí odkaz:
http://arxiv.org/abs/2310.15845
Contrastive learning constitutes an emerging branch of self-supervised learning that leverages large amounts of unlabeled data, by learning a latent space, where pairs of different views of the same sample are associated. In this paper, we propose mu
Externí odkaz:
http://arxiv.org/abs/2302.07077
The study of Music Cognition and neural responses to music has been invaluable in understanding human emotions. Brain signals, though, manifest a highly complex structure that makes processing and retrieving meaningful features challenging, particula
Externí odkaz:
http://arxiv.org/abs/2202.09750
The advent of deep learning has led to the prevalence of deep neural network architectures for monaural music source separation, with end-to-end approaches that operate directly on the waveform level increasingly receiving research attention. Among t
Externí odkaz:
http://arxiv.org/abs/2103.04336
Autor:
Avramidis, Kleanthis, Kratimenos, Agelos, Garoufis, Christos, Zlatintsi, Athanasia, Maragos, Petros
Sound Event Detection and Audio Classification tasks are traditionally addressed through time-frequency representations of audio signals such as spectrograms. However, the emergence of deep neural networks as efficient feature extractors has enabled
Externí odkaz:
http://arxiv.org/abs/2102.06930
Emotion Recognition from EEG signals has long been researched as it can assist numerous medical and rehabilitative applications. However, their complex and noisy structure has proven to be a serious barrier for traditional modeling methods. In this p
Externí odkaz:
http://arxiv.org/abs/2010.16310
Autor:
Kratimenos, Agelos, Avramidis, Kleanthis, Garoufis, Christos, Zlatintsi, Athanasia, Maragos, Petros
Instrument classification is one of the fields in Music Information Retrieval (MIR) that has attracted a lot of research interest. However, the majority of that is dealing with monophonic music, while efforts on polyphonic material mainly focus on pr
Externí odkaz:
http://arxiv.org/abs/1911.12505
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Akademický článek
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Publikováno v:
In Proceedings of the 15th Sound and Music Computing Conference (SMC 2018)
This paper describes MoveSynth, a performance system for two players, who interact with it and collaborate with each other in various ways, including full-body movements, arm postures and continuous gestures, to compose music in real time. The system
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d070c74fcfeb84a4ad922b4c5dbabe6
https://doi.org/10.5281/zenodo.1341738
https://doi.org/10.5281/zenodo.1341738