Harmonic Detection Using Windowed Interpolation Algorithm

This paper discusses the windowed interpolation algorithm for harmonic detection, which demonstrates excellent performance in suppressing spectral leakage and fence effects. The algorithm typically involves applying window functions (such as Hanning, Hamming, or Blackman windows) to reduce spectral leakage, followed by interpolation methods (like quadratic or Gaussian interpolation) between frequency bins to minimize fence effects. Notably, this algorithm finds applications across multiple domains including audio processing, image processing, and biomedical signal processing. In audio applications, it enables effective signal filtering and noise reduction through frequency-domain analysis, resulting in enhanced audio clarity. For image processing, the algorithm facilitates high-quality image scaling operations by preserving frequency components during resampling. In biomedical contexts, it supports precise analysis and recognition of signals such as EEG and ECG through harmonic characteristic extraction, contributing significantly to medical diagnostics. The algorithm's implementation generally involves FFT computation, window function multiplication, peak detection, and phase-corrected interpolation. Thus, the windowed interpolation algorithm represents a highly valuable signal processing technique worthy of further research and practical application.