A Neural Codec Approach for Noise-Robust Bandwidth Expansion

Neural audio codec technology has recently attracted significant attention in various speech processing tasks due to its efficient quantized latent features. In this work, we introduce a novel approach that leverages a pre-trained neural codec network to perform both speech denoising and bandwidth expansion simultaneously. Specifically, we design a conformer-based deep neural network to predict clean codebook indices, which are then used by the pre-trained audio codec model to generate enhanced and bandwidth-expanded audio. We investigated several strategies for generating the clean indices and compared our approach with state-of-the-art methods on the Valentini-Botinhao noisy test set. Experimental results demonstrate that our method achieves performance comparable to leading approaches in noise-robust bandwidth expansion tasks while offering promising improvements in the quality and intelligibility of narrow-band signals. Audio samples are available.