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Month: December 2023

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Explainable Machine Learning

December 11, 2023December 11, 2023 daniel

Speaker: Sara Barahona Quirós. Abstract: Explainable Machine Learning (XAI) refers to the development of machine learning models and algorithms that not only make accurate decisions but also provide understandable and interpretable explanations for those predictions. In traditional machine learning, particularly… Read More

AUDIAS Seminars

Generative Artificial Intelligence: A Global Overview

December 4, 2023December 4, 2023 daniel

Speaker: Diego de Benito Gorrón. Abstract: Generative Artificial Intelligence (GenAI) has made a strong impact on the technological landscape, redefining paradigms and possibilities. This talk offers a panoramic view of GenAI, with a specific focus on Large Language Models (LLMs)… Read More

AUDIAS Seminars

AUDIAS Seminars

Attention-Based Ensemble Systems for Component-Level Audio Deepfake Detection in the ESDD2 Challenge

November 13, 2026

Speaker: Alejandro Delgado Montero. Abstract: The recent Environment-Aware Speech and…

From Supervised Prediction to Unsupervised Discovery: Decoding Latent Epigenetic States via Multi-Omic Manifold Learning

November 6, 2026

Speaker: Alfonso Rincón Pérez. Abstract: Bridging chromatin architecture to transcriptional…

S-DiverSe: Spanish Diverse Speech

October 16, 2026

Speaker: William Fernando López Gavilánez. Abstract: Automatic speech recognition (ASR)…

Transformer-Based Automatic Music Transcription for Piano with Noise Robustness and Mobile Deployment

October 9, 2026

Speaker: Miguel Fernández Lara. Abstract: This work presents the development…

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AUDIAS is a solid research group addressing challenging problems in speech, audio and temporal signals from deep foundations in machine learning and signal processing.

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Highlights

Attention-Based Ensemble Systems for Component-Level Audio Deepfake Detection in the ESDD2 Challenge

November 13, 2026

From Supervised Prediction to Unsupervised Discovery: Decoding Latent Epigenetic States via Multi-Omic Manifold Learning

November 6, 2026
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