32 citations · 56 across the 11 of their papers we have counts for
11 papers
Teager-Kaiser Energy Methods For EEG Feature Extraction In Biomedical Applications
Ioanna Chourdaki, Kleanthis Avramidis, Christos Garoufis +2
Electroencephalography (EEG) signals are inherently non-linear, non-stationary, and vulnerable to noise sources, making the extraction of discriminative features a long-standing ch…
CVSM: Contrastive Vocal Similarity Modeling
Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
The availability of large, unlabeled datasets across various domains has contributed to the development of a plethora of methods that learn representations for multiple target (dow…
Classical Guitar Duet Separation using GuitarDuets -- a Dataset of Real and Synthesized Guitar Recordings
Marios Glytsos, Christos Garoufis, Athanasia Zlatintsi +1
Recent advancements in music source separation (MSS) have focused in the multi-timbral case, with existing architectures tailored for the separation of distinct instruments, overlo…
Sparse Hybrid Linear-Morphological Networks
Konstantinos Fotopoulos, Christos Garoufis, Petros Maragos
We investigate hybrid linear-morphological networks. Recent studies highlight the inherent affinity of morphological layers to pruning, but also their difficulty in training. We pr…
Pre-training Music Classification Models via Music Source Separation
Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
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 en…
Multi-Source Contrastive Learning from Musical Audio
Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
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…
Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation
Kleanthis Avramidis, Christos Garoufis, Athanasia Zlatintsi +1
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 make…
HTMD-Net: A Hybrid Masking-Denoising Approach to Time-Domain Monaural Singing Voice Separation
Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
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…
Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms
Kleanthis Avramidis, Agelos Kratimenos, Christos Garoufis +2
Sound Event Detection and Audio Classification tasks are traditionally addressed through time-frequency representations of audio signals such as spectrograms. However, the emergenc…
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition
Kleanthis Avramidis, Athanasia Zlatintsi, Christos Garoufis +1
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 prov…
Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music
Agelos Kratimenos, Kleanthis Avramidis, Christos Garoufis +2
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 mon…