Reconstruction Algorithm for Speech Signals Based on Compressed Sensing

This article explores a speech signal reconstruction algorithm based on compressed sensing, utilizing the Backpropagation (BP) neural network algorithm for implementation. Compressed sensing is a signal sampling and compression method that enables high-fidelity signal reconstruction through limited sampling and compression. In speech signal reconstruction, compressed sensing algorithms can significantly enhance both the quality and efficiency of signal recovery. The implementation involves using the BP neural network to solve the optimization problems inherent in compressed sensing, which typically model sparse signal recovery through L1-norm minimization. Key functions include signal sparsification, measurement matrix design, and iterative reconstruction using gradient descent optimization in the BP training process. Therefore, this research holds significant theoretical and practical importance for speech signal processing and reconstruction. Additionally, the BP neural network algorithm finds widespread applications in artificial intelligence, making this study both academically and practically valuable for developing efficient signal processing systems.