Conv TasNet

Conv-TasNet is a convolutional neural network architecture designed for single-channel speech separation, aiming to isolate individual speakers from a mixed audio signal. Current research focuses on optimizing Conv-TasNet for real-time applications and low-resource devices through model compression techniques, such as reducing the number of convolutional layers and exploring alternative architectures like U-Net modifications. These efforts are driven by the need for efficient and effective speech separation in applications like hearing aids and real-time communication systems, with recent work demonstrating significant improvements in computational efficiency without sacrificing performance.