Transient Noise Control: A Neural Network Approach with Low-Complexity Computation

Transient noise poses a significant challenge in various applications, requiring effective suppression methods for enhanced performance. This article explores a cutting-edge neural network-based approach tailored for transient noise control. The method boasts low-complexity computation, ensuring efficient implementation across different systems and platforms. The research, available under the DOI 10.3397/IN-2021-11598, introduces an innovative solution that stands out in the realm of noise suppression. By leveraging advanced neural network architectures, the proposed method excels in identifying and mitigating transient noise patterns, contributing to improved signal quality. This breakthrough promises to benefit industries such as telecommunications, audio processing, and automotive engineering, where transient noise can significantly impact overall system performance. The implementation of this method is poised to elevate the standard of noise control in diverse applications, addressing the challenges posed by transient noise with unparalleled precision.