Timbre analysis of music audio signals with convolutional neural networks

@article{Pons2017TimbreAO,
  title={Timbre analysis of music audio signals with convolutional neural networks},
  author={J. Pons and Olga Slizovskaia and R. Gong and E. G{\'o}mez and X. Serra},
  journal={2017 25th European Signal Processing Conference (EUSIPCO)},
  year={2017},
  pages={2744-2748}
}
  • J. Pons, Olga Slizovskaia, +2 authors X. Serra
  • Published 2017
  • Computer Science
  • 2017 25th European Signal Processing Conference (EUSIPCO)
  • 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 CNN architectures. Through this literature overview we discuss which are the crucial points to consider for efficiently learning timbre representations using CNNs. From this discussion we propose a design strategy meant to capture the relevant time-frequency contexts for learning… CONTINUE READING
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