Windowed Discrete Fourier Transform
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The Windowed Discrete Fourier Transform (WDFT) is a widely used spectral analysis method suitable for frequency domain analysis of discrete signals. By selecting appropriate window functions, it effectively mitigates spectral leakage issues resulting from signal truncation. The choice of window function is critical for achieving precise and accurate spectral analysis, requiring careful selection during DFT implementation. Common window functions include Hanning, Hamming, and Blackman windows, each offering different trade-offs between main lobe width and side lobe attenuation. Through WDFT analysis, we gain comprehensive insights into the spectral characteristics of discrete signals, enabling deeper signal analysis and processing capabilities. The implementation typically involves applying the window function to the time-domain signal before performing the DFT operation, which can be efficiently computed using FFT algorithms in practice.
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