8 citations · 9 across the 7 of their papers we have counts for
5 papers · 1 filter
Steering dense music retrieval with open-vocabulary concept discovery
Julien Guinot, Alain Riou, Elio Quinton +1
Controllable music retrieval lets users find music that is, for example, more ambient, less distorted, or without guitar while preserving the other semantic content of an original…
GD-Retriever: Controllable Generative Text-Music Retrieval with Diffusion Models
Julien Guinot, Elio Quinton, György Fazekas
Multimodal contrastive models have achieved strong performance in text-audio retrieval and zero-shot settings, but improving joint embedding spaces remains an active research area.…
SLAP: Siamese Language-Audio Pretraining Without Negative Samples for Music Understanding
Julien Guinot, Alain Riou, Elio Quinton +1
Joint embedding spaces have significantly advanced music understanding and generation by linking text and audio through multimodal contrastive learning. However, these approaches f…
Leave-One-EquiVariant: Alleviating invariance-related information loss in contrastive music representations
Julien Guinot, Elio Quinton, György Fazekas
Contrastive learning has proven effective in self-supervised musical representation learning, particularly for Music Information Retrieval (MIR) tasks. However, reliance on augment…
Foundation Models for Music: A Survey
Yinghao Ma, Anders Øland, Anton Ragni +39
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This com…