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Athanasia Zlatintsi

8 accepted papers

2023

E-Prevention: The ICASSP-2023 Challenge on Person Identification and Relapse Detection from Continuous Recordings of Biosignals

Athanasia Zlatintsi, Panagiotis Paraskevas Filntisis, Niki Efthymiou, Christos Garoufis, George Retsinas, Thomas Sounapoglou +4
ICASSP 2023accepted

The e-Prevention challenge concerns the analysis and processing of long-term continuous recordings of biosignals recorded from wearable sensors, i.e., accelerometers, gyroscopes and heart rate monitors embedded in smartwatches, as well as sleep information and daily step count, in order to extract h…

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2023

Relapse Prediction from Long-Term Wearable Data Using Self-Supervised Learning and Survival Analysis

E. Fekas, Athanasia Zlatintsi, Panagiotis Paraskevas Filntisis, Christos Garoufis, Niki Efthymiou, Petros Maragos
ICASSP 2023accepted

The introduction of biometric signal analysis in psychiatry could potentially reshape the field by making it more accurate, proactive and personalized. Such biosignals usually acquired from wearables encompass the quantification of human behavior and traits. In this study, we use long-term data acqu…

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2022

Enhancing Affective Representations Of Music-Induced Eeg Through Multimodal Supervision And Latent Domain Adaptation

Kleanthis Avramidis, Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
ICASSP 2022accepted

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, particularly of abstract constructs like affect. Moreover,…

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2021

Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms

Kleanthis Avramidis, Agelos Kratimenos, Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
ICASSP 2021accepted

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 the direct use of audio signals for classification…

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2020

An LSTM-Based Dynamic Chord Progression Generation System for Interactive Music Performance

Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
ICASSP 2020accepted

In this paper, we describe an interactive generative music system, designed to handle polyphonic guitar music. We formulate the problem of chord progression generation as a prediction problem. Thus, we propose utilization of an LSTM-based network architecture incorporating neural attention that is a…

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2020

Person Identification Using Deep Convolutional Neural Networks on Short-Term Signals from Wearable Sensors

George Retsinas, Panayiotis Paraskevas Filntisis, Niki Efthymiou, Emmanouil Theodosis, Athanasia Zlatintsi, Petros Maragos
ICASSP 2020accepted

In this work, we explore the discriminating ability of short-term signal patterns (e.g. few minutes long) with respect to the person identification task. We focus on signals recorded by simple wearable devices, such as smart watches, which can measure movements (accelerometer and gyroscope sensors)…

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2018

Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot

Athanasia Zlatintsi, Isidoros Rodomagoulakis, Petros Koutras, A. C. Dometios, Vassilis Pitsikalis, Costas S. Tzafestas +1
ICASSP 2018accepted

We explore new aspects of assistive living on smart human-robot interaction (HRI) that involve automatic recognition and online validation of speech and gestures in a natural interface, providing social features for HRI. We introduce a whole framework and resources of a real-life scenario for elderl…

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2018

Multimodal Visual Concept Learning With Weakly Supervised Techniques

Giorgos Bouritsas, Petros Koutras, Athanasia Zlatintsi, Petros Maragos
CVPR 2018poster

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 paper we use textual cues as means of supervision, i…

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