Singing voice detection with deep recurrent neural networks
Simon Leglaive, Romain Hennequin, Roland Badeau · 2015
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.