Causal Convolution

Causal convolution, a technique restricting neural network computations to past information, is crucial for real-time processing in various applications like speech translation and audio synthesis. Current research focuses on improving the efficiency and performance of causal convolutional networks, often integrating them with other architectures like Transformers and conformers, and employing techniques such as dilated convolutions and dynamic masking to optimize information flow and reduce latency. This work is significant for enabling real-time applications in fields ranging from speech processing and anomaly detection to eye tracking and voice conversion, where immediate responses are essential.