ML Anthology

Source Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract)

Gabriel Mersy, Jin Hong Kuan
AAAI 2021 pp. 15847-15848
doi:10.1609/AAAI.V35I18.17920 /aaai/2021/mersy2021aaai-source/

Abstract

Given recent advances in deep music source separation, we propose a feature representation method that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). We train a depthwise separable convolutional neural network on a challenging electronic dance music (EDM) data set and compare its performance to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data setting compared to the standard single spectrogram approach.

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Cite

Text

Mersy and Kuan. "Source Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17920

Markdown

[Mersy and Kuan. "Source Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/mersy2021aaai-source/) doi:10.1609/AAAI.V35I18.17920

BibTeX

@inproceedings{mersy2021aaai-source,
  title     = {{Source Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract)}},
  author    = {Mersy, Gabriel and Kuan, Jin Hong},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15847-15848},
  doi       = {10.1609/AAAI.V35I18.17920},
  url       = {https://mlanthology.org/aaai/2021/mersy2021aaai-source/}
}