Exploring Data Augmentation for Improved Singing Voice Detection with Neural Networks.

In computer vision, state-of-the-art object recognition sys-tems rely on label-preserving image transformations such as scaling and rotation to augment the training datasets. The additional training examples help the system to learn invariances that are difficult to build into the model, and improve generalization to unseen data. To the best of our knowledge, this approach has not been systematically ex-plored for music signals. Using the problem of singing voice detection with neural networks as an example, we ap-ply a range of label-preserving audio transformations to as-sess their utility for music data augmentation. In line with recent research in speech recognition, we find pitch shift-ing to be the most helpful augmentation method. Com-bined with time stretching and random frequency filtering, we achieve a reduction in classification error between 10 and 30%, reaching the state of the art on two public data-sets. We expect that audio data augmentation would yield significant gains for several other sequence labelling and event detection tasks in music information retrieval. 1.