Optimal Speech Signal Processing Based on Dynamic Multi-Scale Analysis

To enhance the quality of speech signals, this paper introduces a novel speech signal processing method that integrates Dynamic Multi-Scale (DMS) and Adaptive Error Minimization (AEM) techniques. This method significantly enhances noise reduction and signal fidelity in dynamic environments, distinguishing itself from previous approaches through its real-time adaptive filtering, which makes it highly adaptable to complex, non-stationary noise conditions. The proposed method is grounded in dynamic multi-scale analysis, employing multi-scale decomposition of speech signals to optimize their time-frequency characteristics and dynamic adjustments, thereby forming a new noise reduction approach, DMS. Initially, the multi-scale decomposition technique effectively captures the multi-scale features of noisy speech signals. Subsequently, optimizing the time-frequency characteristics and dynamic signal adjustments effectively removes noise while improving the signal’s time-frequency resolution. Finally, the method is further enhanced through the adaptive error minimization algorithm, leading to a more pronounced noise reduction effect. Experimental results demonstrate that the proposed method outperforms the single dynamic multi-scale technique in terms of improving signal-to-noise ratio (SNR).