Extracting Speech Signal Fundamental Frequency Using Autocorrelation Method
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This article discusses how the autocorrelation method can be applied to extract the fundamental frequency from speech signals. A key step in the implementation involves center-clipping, which effectively enhances signal processing. The autocorrelation method analyzes the similarity between a signal and its time-shifted version, commonly used in speech recognition and synthesis systems. Center-clipping serves as a crucial preprocessing technique that helps reduce noise and clutter in the signal by setting amplitudes below a certain threshold to zero. The author provides valuable insights that help readers better understand the practical application of autocorrelation and center-clipping techniques in digital signal processing. Implementation typically involves calculating the autocorrelation function using Fast Fourier Transform (FFT) algorithms and applying threshold-based center-clipping before peak detection to identify the fundamental frequency period.
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