Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Zlatintsi, Athanasia"'
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:
Filntisis, Panagiotis P., Zlatintsi, Athanasia, Efthymiou, Niki, Kalisperakis, Emmanouil, Karantinos, Thomas, Lazaridi, Marina, Smyrnis, Nikolaos, Maragos, Petros
Digital phenotyping is a nascent multidisciplinary field that has the potential to revolutionize psychiatry and its clinical practice. In this paper, we present a rigorous statistical analysis of short-time features extracted from wearable data, duri
Externí odkaz:
http://arxiv.org/abs/2011.02285
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
Autor:
Gkanatsios, Nikolaos, Pitsikalis, Vassilis, Koutras, Petros, Zlatintsi, Athanasia, Maragos, Petros
Detecting visual relationships, i.e. triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch. We introduce a new deeply supervised
Externí odkaz:
http://arxiv.org/abs/1902.05829
Publikováno v:
Proc. IEEE/CVF Conf. Comp. Vis. Patt. Rec. (CVPR) pp. 4914 - 4923 (2018)
Despite the availability of a huge amount of video data accompanied by descriptive texts, it is not always easy to exploit the information contained in natural language in order to automatically recognize video concepts. Towards this goal, in this pa
Externí odkaz:
http://arxiv.org/abs/1712.00796