4 papers
Better audio representations are more brain-like: linking model-brain alignment with performance in downstream auditory tasks
Leonardo Pepino, Pablo Riera, Juan Kamienkowski +1
Artificial neural networks are increasingly powerful models of brain computation, yet it remains unclear whether improving their performance in downstream tasks also makes their in…
Benchmarking Time-localized Explanations for Audio Classification Models
Cecilia Bolaños, Leonardo Pepino, Martin Meza +1
Most modern approaches for audio processing are opaque, in the sense that they do not provide an explanation for their decisions. For this reason, various methods have been propose…
A Dataset for Automatic Assessment of TTS Quality in Spanish
Alejandro Sosa Welford, Leonardo Pepino
This work addresses the development of a database for the automatic assessment of text-to-speech (TTS) systems in Spanish, aiming to improve the accuracy of naturalness prediction…
EnCodecMAE: Leveraging neural codecs for universal audio representation learning
Leonardo Pepino, Pablo Riera, Luciana Ferrer
The goal of universal audio representation learning is to obtain foundational models that can be used for a variety of downstream tasks involving speech, music and environmental so…