Data Augmentation for Monaural Singing Voice Separation Based on Variational Autoencoder-Generative Adversarial Network
Boxin He, Shengbei Wang, Weitao Yuan, Jianming Wang, Masashi Unoki · 2019
Random mixing and circularly shifting for augmenting the training set are used to improve the separation effect of deep neural network (DNN)-based monaural singing voice separation (MSVS). However, these manual methods are based on unrealistic assumptions that two sources in the mixture are independent of each other, which limits the separation effect. This paper proposes a data augmentation method based on variational autoencoder (VAE) and generative adversarial network (GAN), which is called as VAE-GAN. The VAE models the observed spectra of sources (vocal and music) separately and reconstructs new spectra from the latent space. The GAN's discriminator is introduced to measure the correlation between the latent variables of the vocal and music generated by the VAE probability encoder. This adversarial mechanism in VAE's latent space could learn the synthetic likelihood and ultimately decode high quality spectra samples, which further improves the separation effect of general MSVS networks.