Time Domain Speech Separation

Time-domain speech separation aims to isolate individual voices from a mixture of sounds, directly in the time domain, unlike frequency-domain methods. Current research emphasizes developing efficient and robust deep learning models, such as transformer-based architectures and modified U-Net networks, often incorporating recurrent or memory mechanisms to handle long-range dependencies and improve real-time performance. These advancements are crucial for improving speech recognition in challenging acoustic environments and enabling applications like real-time transcription of multi-speaker conversations and hearing aid technology. Furthermore, research explores novel training strategies and loss functions to address issues like channel mismatch and zero-energy target signals, leading to more accurate and reliable separation.