Signal processing method combining multilevel spectrum decomposition and A-weighting

With the advancement of complex signal processing technologies, effectively extracting meaningful components from signals, particularly pulse and harmonic components, has become a critical research focus. This study introduces a signal analysis approach that combines multi-level spectrum decomposition (MSD) with A-weighted filtering. Through MSD, the signal is broken down into multiple frequency bands, enabling precise extraction of time-frequency characteristics across different ranges. Building on this, A-weighting is applied to adjust the spectrum of each band, mimicking the human ear's sensitivity to various frequency components, thereby optimizing the signal analysis. By isolating and extracting pulse signals, periodic pulses, and harmonics, this method significantly enhances the ability to identify components in complex signal processing. Experimental results demonstrate that the MSD-A-weighting combination can accurately extract key signal components and improve perceptual quality, offering wide application potential in areas such as noise reduction, fault diagnosis, and audio signal processing.