8 citations · 19 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
Exploring trends in audio mixes and masters: Insights from a dataset analysis
Angeliki Mourgela, Elio Quinton, Spyridon Bissas +2
We present an analysis of a dataset of audio metrics and aesthetic considerations about mixes and masters provided by the web platform MixCheck studio. The platform is designed for…
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…
MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models
Benno Weck, Ilaria Manco, Emmanouil Benetos +3
Multimodal models that jointly process audio and language hold great promise in audio understanding and are increasingly being adopted in the music domain. By allowing users to que…
Robust Lossy Audio Compression Identification
Hendrik Vincent Koops, Gianluca Micchi, Elio Quinton
Previous research contributions on blind lossy compression identification report near perfect performance metrics on their test set, across a variety of codecs and bit rates. Howev…
Semi-Supervised Contrastive Learning of Musical Representations
Julien Guinot, Elio Quinton, György Fazekas
Despite the success of contrastive learning in Music Information Retrieval, the inherent ambiguity of contrastive self-supervision presents a challenge. Relying solely on augmentat…