Mixup Augmentation for Generalizable Speech Separation

Deep learning has advanced the state of the art of single-channel speech separation. However, separation models may overfit the training data and generalization across datasets is still an open problem in real-world conditions with noise. In this paper we address the generalization problem with Mixup as data augmentation approach. Mixup creates new training examples from linear combinations of samples during mini-batch training. We propose four variations of Mixup and assess the improved generalization of a speech separation model, DPRNN, with cross-corpus evaluation on LibriMix, TIMIT and VCTK datasets. DPRNN allows efficient modelling of longer input sequences by splitting the learnt representation from input mixture segment into small chunks and performing intra and inter chunk operations iteratively. We show that training DPRNN with the proposed Data-only Mixup augmentation variation improves performance on an unseen dataset in noisy conditions when compared to the baseline SpecAugment augmented models, while having comparable performance on the source dataset.