8 citations · 30 across the 6 of their papers we have counts for
6 papers
Contrastive Learning for Cross-modal Artist Retrieval
Andres Ferraro, Jaehun Kim, Sergio Oramas +2
Music retrieval and recommendation applications often rely on content features encoded as embeddings, which provide vector representations of items in a music dataset. Numerous com…
Supervised and Unsupervised Learning of Audio Representations for Music Understanding
Matthew C. McCallum, Filip Korzeniowski, Sergio Oramas +2
In this work, we provide a broad comparative analysis of strategies for pre-training audio understanding models for several tasks in the music domain, including labelling of genre,…
Artist Similarity with Graph Neural Networks
Filip Korzeniowski, Sergio Oramas, Fabien Gouyon
Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collections of music. In this paper, we present a hybrid a…
Multimodal Metric Learning for Tag-based Music Retrieval
Minz Won, Sergio Oramas, Oriol Nieto +2
Tag-based music retrieval is crucial to browse large-scale music libraries efficiently. Hence, automatic music tagging has been actively explored, mostly as a classification task,…
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
Emilia Gómez, Carlos Castillo, Vicky Charisi +23
This document contains the outcome of the first Human behaviour and machine intelligence (HUMAINT) workshop that took place 5-6 March 2018 in Barcelona, Spain. The workshop was org…
On Evaluation Validity in Music Autotagging
Fabien Gouyon, Bob L. Sturm, Joao Lobato Oliveira +2
Music autotagging, an established problem in Music Information Retrieval, aims to alleviate the human cost required to manually annotate collections of recorded music with textual…