no code implementations • 12 Aug 2023 • Andres Ferraro, Jaehun Kim, Sergio Oramas, Andreas Ehmann, Fabien Gouyon
We demonstrate our method successfully combines complementary information from diverse modalities, and is more robust to missing modality data (i. e., it better handles the retrieval of artists with different modality embeddings than the query artist's).
no code implementations • 22 Feb 2023 • Andres Ferraro, Gustavo Ferreira, Fernando Diaz, Georgina Born
After demonstrating that existing metrics do not center culture, we introduce a new metric, commonality, that measures the degree to which recommendations familiarize a given user population with specified categories of cultural content.
no code implementations • 2 Aug 2022 • Andres Ferraro, Gustavo Ferreira, Fernando Diaz, Georgina Born
Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience.
1 code implementation • 25 Apr 2022 • Fernando Diaz, Andres Ferraro
Offline evaluation of information retrieval and recommendation has traditionally focused on distilling the quality of a ranking into a scalar metric such as average precision or normalized discounted cumulative gain.
1 code implementation • 1 Jul 2021 • Eduardo Fonseca, Andres Ferraro, Xavier Serra
Recent studies have put into question the commonly assumed shift invariance property of convolutional networks, showing that small shifts in the input can affect the output predictions substantially.
1 code implementation • 1 Apr 2021 • Andres Ferraro, Xavier Favory, Konstantinos Drossos, Yuntae Kim, Dmitry Bogdanov
Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information.
1 code implementation • 30 Jan 2021 • Andres Ferraro, Yuntae Kim, Soohyeon Lee, Biho Kim, Namjun Jo, Semi Lim, Suyon Lim, Jungtaek Jang, Sehwan Kim, Xavier Serra, Dmitry Bogdanov
We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649, 091tracks and 148, 826 associated playlists annotated by 30, 652 different tags.
1 code implementation • 17 Aug 2020 • Andres Ferraro, Dietmar Jannach, Xavier Serra
Specifically, we analyze to what extent algorithms of different types may lead to concentration effects over time.
7 code implementations • 1 Jun 2020 • Minz Won, Andres Ferraro, Dmitry Bogdanov, Xavier Serra
Recent advances in deep learning accelerated the development of content-based automatic music tagging systems.
Ranked #1 on Music Auto-Tagging on MagnaTagATune (clean)
Music Auto-Tagging Audio and Speech Processing Sound
no code implementations • 12 Nov 2019 • Andres Ferraro, Dmitry Bogdanov, Xavier Serra, Jason Yoon
Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across different styles.
no code implementations • 12 Nov 2019 • Andres Ferraro, Dmitry Bogdanov, Xavier Serra, Jay Ho Jeon, Jason Yoon
Automatic tagging of music is an important research topic in Music Information Retrieval and audio analysis algorithms proposed for this task have achieved improvements with advances in deep learning.
2 code implementations • 21 Oct 2019 • Andres Ferraro, Kjell Lemström
The importance of repetitions in music is well-known.