5 accepted papers
Existing multi-modal automatic modulation recognition (AMR) methods primarily focus on exploiting multi-view representations of raw signal data to improve performance, but still struggle to effectively model and exploit high-level human prior knowledge. Although recent studies attempt to introduce l
Robust signal enhancement at extremely low SNR is fundamentally challenging because noise becomes strongly entangled with the signal and corrupts local time–frequency (TF) evidence. In this regime, fixed resolution short-time Fourier transform (STFT) enhancement with purely data driven convolutional…
Robust signal enhancement under non-stationary and low SNR conditions remains challenging, as methods based on the short-time Fourier transform (STFT) with fixed resolution struggle to represent complex and time–frequency structures. While leveraging the fractional domain as an auxiliary view offers
Deep learning-based speech enhancement (SE) methods focus on reconstructing speech from the time or frequency domain. However, these domains cannot provide enough information to capture the dynamics of non-stationary signals accurately. To enrich information, this work proposes a multi-view fusion S
Deep learning-based speech enhancement (SE) methods predominantly draw upon two architectural frameworks: generative adversarial networks and diffusion models. In the realm of SE, capturing the local and global relations between signal frames is crucial for the success of these methods. These framew