Noise Tolerance

Noise tolerance in various applications, from speech recognition to medical diagnostics, focuses on developing systems robust to interfering signals or inaccurate data. Current research emphasizes the use of deep neural networks, including convolutional and recurrent architectures, and diffusion models, often incorporating techniques like robust loss functions and noise-aware training to improve performance in noisy environments. These advancements are crucial for improving the reliability and accuracy of AI systems across diverse fields, ranging from healthcare (e.g., automated audiology) to communication technologies (e.g., speech enhancement). The development of noise-tolerant algorithms is essential for deploying reliable AI systems in real-world scenarios where perfect data is unavailable.