Visualitza per autor "Pons Puig, Jordi"

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  • Gong, Rong; Pons Puig, Jordi; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2017)
    We approach the singing phrase audio to score matching problem by using phonetic and duration information – with a focus on studying the jingju a cappella singing case. We argue that, due to the existence of a basic ...
  • Pons Puig, Jordi; Serra, Xavier (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    Many researchers use convolutional neural networks with small rectangular filters for music (spectrograms) classification. First, we discuss why there is no reason to use this filters setup by default and second, we point ...
  • Pons Puig, Jordi; Lidy, Thomas; Serra, Xavier (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    A common criticism of deep learning relates to the difficulty in understanding the underlying relationships that/nthe neural networks are learning, thus behaving like a black-box. In this article we explore various ...
  • Fonseca, Eduardo; Pons Puig, Jordi; Favory, Xavier; Font Corbera, Frederic; Bogdanov, Dmitry; Ferraro, Andrés; Oramas, Sergio; Porter, Alastair; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2017)
    Openly available datasets are a key factor in the advancement of data-driven research approaches, including many of the ones used in sound and music computing. In the last few years, quite a number of new audio datasets ...
  • Pons Puig, Jordi; Gong, Rong; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2017)
    This paper introduces a new score-informed method for the segmentation of jingju a cappella singing phrase into syllables. The proposed method estimates the most likely sequence of syllable boundaries given the estimated ...
  • Pons Puig, Jordi; Slizovskaia, Olga; Gómez Gutiérrez, Emilia, 1975-; Serra, Xavier (European Association for Signal Processing (EURASIP), 2017)
    The focus of this work is to study how to efficiently tailor Convolutional Neural Networks (CNNs) towards learning timbre representations from log-mel magnitude spectrograms. We first review the trends when designing ...