9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network

      Preprint
      , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Identification and extraction of singing voice from within musical mixtures is a key challenge in source separation and machine audition. Recently, deep neural networks (DNN) have been used to estimate 'ideal' binary masks for carefully controlled cocktail party speech separation problems. However, it is not yet known whether these methods are capable of generalizing to the discrimination of voice and non-voice in the context of musical mixtures. Here, we trained a convolutional DNN (of around a billion parameters) to provide probabilistic estimates of the ideal binary mask for separation of vocal sounds from real-world musical mixtures. We contrast our DNN results with more traditional linear methods. Our approach may be useful for automatic removal of vocal sounds from musical mixtures for 'karaoke' type applications.

          Related collections

          Author and article information

          Journal
          17 April 2015
          Article
          1504.04658
          13a4c168-9e0f-4cb3-8ddf-6fe329619ea4

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          68Txx
          cs.SD cs.LG cs.NE

          Comments

          Comment on this article