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Exploring Representation Autoencoders for Text-to-Audio Generation
Bernardo V. Miranda, Hugo Malard, Gaël Richard +2
Representation autoencoders (RAE), created by pairing a frozen pretrained self-supervised learning (SSL) encoder with a trained decoder, have recently demonstrated state-of-the-art…
Probing Large Audio-Language Models for Compositional Understanding of Sounding Actions
Michel Olvera, Paraskevas Stamatiadis, Changhong Wang +1
Large audio-language models (LALMs) excel at understanding and reasoning tasks over atomic sound events, yet their ability to infer higher-level human activities from such fine-gra…
The Internet Archive Music Dataset
Paraskevas Stamatiadis, Bernardo V Miranda, Clémentine Berger +3
We introduce the Internet Archive Music Dataset (IAMD), a large-scale collection of captioned music segments derived from the Internet Archive. To the best of our knowledge, IAMD c…
Unlocking Spatial Grounding in Large Audio-Visual Retrieval models
Hugo Malard, Michel Olvera, Sanjeel Parekh +3
Weak supervision sets a practical regime for audio-visual sound source localization as dense spatial annotations are costly to obtain at scale. The task, however, remains challengi…
Taylor-SWFT: fast discrete Statistical Wave Field Theory using Taylor expansion for late reverberation Work under review
Marius Rodrigues, Louis Lalay, Roland Badeau +2
Dynamic room acoustic simulation aims to render the acoustic effects of an environment in real time while accounting for potentially moving sources and receivers. In this context,…
S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization
Zineb Lahrichi, Gaëtan Hadjeres, Gaël Richard +1
Neural audio compression models have recently achieved extreme compression rates, enabling efficient latent generative modeling. Conversely, latent generative models have been appl…