2 citations · 4 across the 6 of their papers we have counts for
10 papers
BrainCodes: A Lightweight EEG Foundation Model over Discrete Neural Codes
Kleanthis Avramidis, Ivan Tashev, Dimitra Emmanouilidou
Recent electroencephalography (EEG) foundation models increasingly emphasize model scale, potentially limiting their deployment on resource-constrained devices and in everyday envi…
Cognitive Load Estimation Using Brain Foundation Models and Interpretability for BCIs
Deeksha M. Shama, Dimitra Emmanouilidou, Ivan J. Tashev
Accurately monitoring cognitive load in real time is critical for Brain-Computer Interfaces (BCIs) that adapt to user engagement and support personalized learning. Electroencephalo…
SALAD-VAE: Semantic Audio Compression with Language-Audio Distillation
Sebastian Braun, Hannes Gamper, Dimitra Emmanouilidou
Modern generative and multimodal models increasingly rely on compact latent representations that trade and balance semantic richness with high-fidelity reconstruction. We introduce…
Quality Over Quantity? LLM-Based Curation for a Data-Efficient Audio-Video Foundation Model
Ali Vosoughi, Dimitra Emmanouilidou, Hannes Gamper
Integrating audio and visual data for training multimodal foundational models remains a challenge. The Audio-Video Vector Alignment (AVVA) framework addresses this by considering A…
Addressing Emotion Bias in Music Emotion Recognition and Generation with Frechet Audio Distance
Yuanchao Li, Azalea Gui, Dimitra Emmanouilidou +1
The complex nature of musical emotion introduces inherent bias in both recognition and generation, particularly when relying on a single audio encoder, emotion classifier, or evalu…
Adapting Frechet Audio Distance for Generative Music Evaluation
Azalea Gui, Hannes Gamper, Sebastian Braun +1
The growing popularity of generative music models underlines the need for perceptually relevant, objective music quality metrics. The Frechet Audio Distance (FAD) is commonly used…
Training Audio Captioning Models without Audio
Soham Deshmukh, Benjamin Elizalde, Dimitra Emmanouilidou +3
Automated Audio Captioning (AAC) is the task of generating natural language descriptions given an audio stream. A typical AAC system requires manually curated training data of audi…
Investigations in Audio Captioning: Addressing Vocabulary Imbalance and Evaluating Suitability of Language-Centric Performance Metrics
Sandeep Kothinti, Dimitra Emmanouilidou
The analysis, processing, and extraction of meaningful information from sounds all around us is the subject of the broader area of audio analytics. Audio captioning is a recent add…
CURE Dataset: Ladder Networks for Audio Event Classification
Harishchandra Dubey, Dimitra Emmanouilidou, Ivan J. Tashev
Audio event classification is an important task for several applications such as surveillance, audio, video and multimedia retrieval etc. There are approximately 3M people with hea…
Predicting word error rate for reverberant speech
Hannes Gamper, Dimitra Emmanouilidou, Sebastian Braun +1
Reverberation negatively impacts the performance of automatic speech recognition (ASR). Prior work on quantifying the effect of reverberation has shown that clarity (C50), a parame…