Search Results for author: Andres Ferraro

Found 12 papers, 7 papers with code

Contrastive Learning for Cross-modal Artist Retrieval

no code implementations12 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).

Contrastive Learning Retrieval

Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship

no code implementations22 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.

Cultural Vocal Bursts Intensity Prediction Fairness +1

Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship

no code implementations2 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.

Cultural Vocal Bursts Intensity Prediction Fairness +2

Offline Retrieval Evaluation Without Evaluation Metrics

1 code implementation25 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.

Information Retrieval Retrieval

Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks

1 code implementation1 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.

Enriched Music Representations with Multiple Cross-modal Contrastive Learning

1 code implementation1 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.

Contrastive Learning Genre classification

Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging

1 code implementation30 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.

Audio Signal Processing Collaborative Filtering +6

Exploring Longitudinal Effects of Session-based Recommendations

1 code implementation17 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.

Re-Ranking Session-Based Recommendations

Evaluation of CNN-based Automatic Music Tagging Models

7 code implementations1 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.

Music Auto-Tagging Audio and Speech Processing Sound

Artist and style exposure bias in collaborative filtering based music recommendations

no code implementations12 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.

Collaborative Filtering Music Recommendation

How Low Can You Go? Reducing Frequency and Time Resolution in Current CNN Architectures for Music Auto-tagging

no code implementations12 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.

Information Retrieval Music Auto-Tagging +2

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