Singing voice detection with deep recurrent neural networks

Simon Leglaive; Romain Hennequin; Roland Badeau

ICASSP 2015accepted0 citations

Singing voice detection with deep recurrent neural networks

Simon Leglaive, Romain Hennequin, Roland Badeau

Abstract

In this paper, we propose a new method for singing voice detection based on a Bidirectional Long Short-Term Memory (BLSTM) Recurrent Neural Network (RNN). This classifier is able to take a past and future temporal context into account to decide on the presence/absence of singing voice, thus using the inherent sequential aspect of a short-term feature extraction in a piece of music. The BLSTM-RNN contains several hidden layers, so it is able to extract a simple representation fitted to our task from low-level features. The results we obtain significantly outperform state-of-the-art methods on a common database.

BibTeX

@inproceedings{icassp2015_singingvoicedete,
  title = {Singing voice detection with deep recurrent neural networks},
  author = {Simon Leglaive and Romain Hennequin and Roland Badeau},
  booktitle = {ICASSP 2015},
  year = {2015}
}