Exploring the Differences and Applications of Signal Processing Technologies Based on Domestic Large Models

The large model technology has revolutionized the field of signal processing, providing new ideas and methods for solving complex signal processing problems. This study delves into the performance of large models in typical application scenarios such as speech enhancement, image super-resolution reconstruction, video codec optimization, and industrial IoT anomaly detection. Experimental data shows that the large model approach surpasses traditional methods by an average of 18.7% in key indicators such as processing accuracy, computational efficiency, and system robustness. Notably, in the low-quality speech recognition task with a signal-to-noise ratio of -5dB, the accuracy is improved by 18.6 percentage points. These achievements not only advance signal processing technology but also demonstrate the enormous potential of large models in practical applications. Future research will focus on model structure optimization, generalization ability enhancement, and innovative applications in broader fields.