Abstract

Combining different models is a common strategy to build a good audio source separation system. In this work, we combine two powerful deep neural networks for audio single channel source separation (SCSS). Namely, we combine fully convolutional neural networks (FCNs) and recurrent neural networks, specifically, bidirectional long short-term memory recurrent neural networks (BLSTMs). FCNs are good at extracting useful features from the audio data and BLSTMs are good at modeling the temporal structure of the audio signals. Our experimental results show that combining FCNs and BLSTMs achieves better separation performance than using each model individually.

Bibtex 


@conference{Grais_2018b,
  title = {Combining Fully Convolutional and Recurrent Neural Networks for Single Channel Audio Source Separation},
  author = {Grais, E. M. and Plumbley, M. D.},
  booktitle = {Audio Engineering Society Convention 144},
  month = may,
  year = {2018},
  address = {Milan, Italy},
  openaccess = {http://epubs.surrey.ac.uk/846033/},
  keywords = {"maruss"}
}

Supplementary Material

Download the poster presented by Emad M. Grais at the 144th AES Convention.