Frequency band recalibration spectrogram

• The paper presents a time–frequency analysis method with filter adaptive iteration. • The proposed method allows for a balance of overall and detailed information. • FBRS is more specific in depicting vital but localized time–frequency information. • Simulation data and three real cases validate the proposed method. Extracting valuable features from a signal’s Region of Interest (ROI) is critical for advanced signal analysis. However, existing methods often require domain-specific design assumptions and fixed filter structures, limiting their adaptability and generalization. To address these limitations, this paper proposes the Frequency Band Recalibration Spectrogram (FBRS), a novel time–frequency analysis method based on the wavelet packet energy distribution. FBRS adaptively optimizes filter bandwidth and dynamically generates filter distributions based solely on a signal’s intrinsic frequency characteristics. By iteratively reconstructing wavelet packet components and refining frequency resolution, FBRS adaptively constructs a signal-driven time–frequency representation by dynamically recalibrating frequency bands based on intrinsic energy distribution. While this results in improved resolution in important regions, the core contribution lies in its adaptive and data-driven spectrogram generation mechanism. To verify the effectiveness of the proposed method, extensive experiments were conducted on simulated signals and three real signals from multiple practical application scenarios. The experimental results demonstrate that FBRS is theoretically innovative and exhibits strong adaptability and descriptive resolution in various signal analysis scenarios. The adaptability and flexibility of the proposed method enable it to cross different domains, providing a new perspective and a powerful tool for signal analysis. The code of the proposed method has been made public in https://github.com/Qinr1026/Frequency-Band-Recalibration-Spectrogram .