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20212026
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cs.SD2026

AI-Generated Music Detection in Broadcast Monitoring

David López-Ayala, Asier Cabello, Pablo Zinemanas +2

AI music generators have advanced to the point where their outputs are often indistinguishable from human compositions. While detection methods have emerged, they are typically des…

cs.SD2025

Domain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification

Emiliano Acevedo, Martín Rocamora, Magdalena Fuentes

Audio-text models are widely used in zero-shot environmental sound classification as they alleviate the need for annotated data. However, we show that their performance severely dr…

cs.SD2024

Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio

Pablo Alonso-Jiménez, Leonardo Pepino, Roser Batlle-Roca +4

We present PECMAE, an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoen…

cs.SD2023

Adapting Meter Tracking Models to Latin American Music

Lucas S. Maia, Martín Rocamora, Luiz W. P. Biscainho +1

Beat and downbeat tracking models have improved significantly in recent years with the introduction of deep learning methods. However, despite these improvements, several challenge…

cs.SD2021

Soundata: A Python library for reproducible use of audio datasets

Magdalena Fuentes, Justin Salamon, Pablo Zinemanas +6

Soundata is a Python library for loading and working with audio datasets in a standardized way, removing the need for writing custom loaders in every project, and improving reprodu…