Exploiting Constant-Q Transform and Its Variant in Light-Weight Neural Network Framework for Artificial Bandwidth Extension

Artificial Bandwidth Extension (ABE) enhances narrowband speech quality by reconstructing the lost high-frequency components essential for clarity and naturalness. In this work, we propose a novel ABE framework that integrates the constantQ Transform (CQT) and its variant within a lightweight neural network. Unlike traditional methods relying on the short-time Fourier transform (STFT), our approach leverages CQT's logarithmic frequency scaling and superior low-frequency resolution to better align with human auditory perception. Two CQTbased feature extraction schemes are introduced: a standard method that extracts narrowband (NB) CQT representations and a modified variant that employs a stacking and masking operation to compensate for missing high-frequency content. A compact Multi-Layer Perceptron (MLP) is then trained to map the extracted features to full wideband (WB) spectral representations. Phase reconstruction is achieved using either spectral folding or spectral shifting in conjunction with inverse CQT (iCQT), enabling effective reconstruction of the time-domain speech signal. Evaluations on the TIMIT dataset show that our model with modified CQT and spectral folding outperforms traditional methods, achieving lower Log Spectral Distance (LSD) and Visual Geometry Group (VGG) distance and higher Virtual Speech Quality Objective Listener (ViSQOL) values. Additionally, subjective evaluations using the MUSHRA framework validate the improvements in perceptual quality offered by the proposed approach.