3 accepted papers
In this paper, we introduce a discriminative vector learning method and apply it to single-channel speech separation. First, speech samples are transformed into discriminative vectors using two backbone networks. These vectors are easily separated by simple clustering algorithms. Among them, vectors…
The time-domain speech separation methods adopting deep learning have obtained impressive performance. However, the computational complexity, model size, and performance are still the challenges for the implementation on real-time low-resource devices. In this paper, we introduce a lightweight yet e…
Mutual learning, the related idea to knowledge distillation, is a group of untrained lightweight networks, which simultaneously learn and share knowledge to perform tasks together during training. In this paper, we propose a novel mutual learning approach, namely selective mutual learning. This is t…