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Listar Congressos (Departament de Tecnologies de la Informació i les Comunicacions) por autoría

Listar Congressos (Departament de Tecnologies de la Informació i les Comunicacions) por autoría "Urbano, Julián"

Mostrando ítems 1-8 de 8

MelodyShape at MIREX 2014 Symbolic Melodic Similarity

Urbano, Julián (Music Information Retrieval Evaluation eXchange (MIREX), 2014)

This short paper describes our three submissions to the 2014 edition of the MIREX Symbolic Melodic Similarity task. All three submissions rely on a geometric model that represents melodies as spline curves in the pitch-time ...
MelodyShape at MIREX 2015 Symbolic Melodic Similarity

Urbano, Julián (Music Information Retrieval Evaluation eXchange (MIREX), 2015)

This short paper describes our three submissions to the 2015 edition of the MIREX Symbolic Melodic Similarity task. They all rely on a geometric model that represents melodies as spline curves in the pitch-time plane. The ...
MIREX 2013 Symbolic melodic similarity: a geometric model supported with hybrid sequence alignment

Urbano, Julián (Music Information Retrieval Evaluation eXchange (MIREX), 2013)

This short paper describes our three submissions to the 2013 edition of the MIREX Symbolic Melodic Similarity task. All three submissions rely on a geometric model that represents melodies as spline curves in the pitch-time ...
A plan for sustainable MIR evaluation

McFee, Brian; Humphrey, Eric J.; Urbano, Julián (International Society for Music Information Retrieval (ISMIR), 2016)

The Music Information Retrieval Evaluation eXchange (MIREX) is a valuable community service, having established standard datasets, metrics, baselines, methodologies, and infrastructure for comparing MIR methods. While MIREX ...
The AcousticBrainz genre dataset: Multi-source, multi-level, multi-label, and large-scale

Bogdanov, Dmitry; Porter, Alastair; Schreiber, Hendrik; Urbano, Julián; Oramas, Sergio (International Society for Music Information Retrieval (ISMIR), 2019)

This paper introduces the AcousticBrainz Genre Dataset, a large-scale collection of hierarchical multi-label genre annotations from different metadata sources. It allows researchers to explore how the same music pieces ...
The MediaEval 2017 AcousticBrainz genre task: content-based music genre recognition from multiple sources

Bogdanov, Dmitry; Porter, Alastair; Urbano, Julián; Schreiber, Hendrik (CEUR Workshop Proceedings, 2017)

This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition ...
The MediaEval 2018 AcousticBrainz genre task: content-based music genre recognition from multiple sources

Bogdanov, Dmitry; Porter, Alastair; Urbano, Julián; Schreiber, Hendrik (CEUR Workshop Proceedings, 2018)

This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition ...
What is the effect of audio quality on the robustness of MFCCs and chroma features?

Urbano, Julián; Bogdanov, Dmitry; Herrera Boyer, Perfecto, 1964-; Gómez Gutiérrez, Emilia, 1975-; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2014)

Music Information Retrieval is largely based on descriptors computed from audio signals, and in many practical applications they are to be computed on music corpora containing audio files encoded in a variety of lossy ...