Simulation of Sound Signal Analysis Model in Complex Environments Based on Deep Learning Algorithms

In modern applications such as intelligent monitoring, health diagnosis, and human-computer interaction, accurate analysis and recognition of sound signals in complex environments have become crucial. This study explores a sound signal analysis method based on DL (Deep Learning) and verifies it in a simulation environment. We have designed a deep neural network structure based on CNN (Convolutional Neural Network) for complex acoustic scenes. By analyzing the time-frequency characteristics of sound signals, the model can effectively capture the local frequency characteristics and long-term time dependence of sound. In order to verify the performance of the model in practical scenarios, we constructed a physics based sound signal simulation environment, simulating multi source interference, environmental noise, and other acoustic effects in the real world. The experimental results show that our model exhibits significant advantages in various evaluation indicators compared to traditional sound processing methods. Especially in high noise and multi source environments, the model exhibits excellent robustness. Overall, this study provides an innovative and effective method for analyzing sound signals in complex environments, and lays a solid foundation for future deployment in practical applications.