7 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation
Ilaria Manco, Benno Weck, SeungHeon Doh +10
We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models. The dataset…