Browsing by Author "Won, Minz"

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  • Won, Minz; Chun, Sanghyuk; Nieto Caballero, Oriol; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2019)
    In this paper, we introduce the Harmonic Convolutional Neural Network (Harmonic CNN), a music representation model that exploits the inherent harmonic structure of audio signals. The proposed model outperforms previous ...
  • Won, Minz; Chun, Sanghyuk; Nieto Caballero, Oriol (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    We introduce a trainable front-end module for audio repre- sentation learning that exploits the inherent harmonic struc- ture of audio signals. The proposed architecture, composed of a set of filters, compels the ...
  • Bogdanov, Dmitry; Porter, Alastair; Tovstogan, Philip; Won, Minz (CEUR Workshop Proceedings, 2019)
    This paper provides an overview of the Emotion and Theme recognition in Music task organized as part of the MediaEval 2019 Benchmarking Initiative for Multimedia Evaluation. The goal of this task is to automatically ...
  • Korzeniowski, Filip; Nieto Caballero, Oriol; McCallum, Matthew C.; Won, Minz; Oramas, Sergio; Schmidt, Erik M. (International Society for Music Information Retrieval (ISMIR), 2020)
    The mood of a song is a highly relevant feature for exploration and recommendation in large collections of music. These collections tend to require automatic methods for predicting such moods. In this work, we show that ...
  • Bogdanov, Dmitry; Won, Minz; Tovstogan, Philip; Porter, Alastair; Serra, Xavier (2019)
    We present the MTG-Jamendo Dataset, a new open dataset for music auto-tagging. It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. The dataset contains ...
  • Kim, Jaehun; Won, Minz; Serra, Xavier; Liem, Cynthia C. S. (ACM Association for Computer Machinery, 2018)
    The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more ...