Scientific Development Research

Digital signal processing technology is increasingly widely used in speech denoising algorithms, and it plays a crucial role especially in improving the clarity of speech signals and effectively suppressing noise. With the diversification of speech processing needs, traditional denoising methods have been difficult to meet the requirements of high efficiency and accuracy. This paper explores the optimization paths of digital signal processing technology in speech denoising, with a focus on analyzing the applications of filtering technology, time-frequency analysis methods, and deep learning in denoising. By comparing the performance of different optimized algorithms, a denoising scheme suitable for real-world environments is proposed, aiming to provide effective technical support for the field of speech processing. Experimental results show that the optimized algorithm has improved both in denoising effect and computational efficiency, providing a theoretical basis and technical guarantee for enhancing speech clarity.

Digital signal processing; Speech denoising; Filtering technology; Deep learning; Time-frequency analysis