5 accepted papers
In recent years, there has been increasing attention on the capabilities of large-scale models, particularly in handling complex tasks that small-scale models are unable to perform. Notably, large language models (LLMs) have demonstrated ``intelligent'' abilities such as complex reasoning and abstra…
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems and cyber-physical systems. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Pe…
Most of the deep learning based speech enhancement methods focus on the modeling of complicated relationship between the noisy speech and the clean speech without the consideration of noise information. In order to cope with various complex noise scenes, we introduce a novel enhancement architecture…
Convolution neural networks (CNNs) have been achieving increasing attention for the artificial bandwidth extension (ABE) task recently. However, these methods use the flipped low-frequency phase to reconstruct speech signals, which may lead to the well-known invalid short-time Fourier Transform (STF…
The data driven speech enhancement approaches using regression-based deep neural network usually result in enormous number of model parameters, which increase the computational load and the difficulty of model training. In order to improve the model efficiency, we propose a densely connected network…