Deep learning-based speech enhancement via adaptive Trans-UNet with novel loss function using enhanced aquila optimization algorithm

Key Points

• Speech enhancement demonstrates improved performance due to a novel loss function, leading to clearer audio output.
• Achieving a 15% reduction in background noise highlights the effectiveness of the aquila optimization algorithm.
• The adaptive Trans-UNet approach enables real-time processing of audio signals for various applications.
• This method emphasizes the need for advanced loss functions in deep learning audio processing, paving the way for future innovations.