Pitch Period Extraction Algorithm in Speech Signal Processing

This paper provides a detailed introduction to the pitch period extraction algorithm for speech signal processing. We employ the traditional autocorrelation method combined with three-level center clipping preprocessing technique to achieve more accurate and reliable pitch period extraction.

Pitch period extraction is a crucial step in speech signal processing, used for analyzing and identifying fundamental frequency components in speech signals. In the traditional autocorrelation approach, we estimate pitch period by calculating the signal's autocorrelation function. However, due to the presence of noise and variations in speech signals, pitch extraction isn't always accurate. Therefore, we implement three-level center clipping as a preprocessing step to reduce noise impact and improve extraction accuracy.

Three-level center clipping is a common signal processing technique that suppresses noise by limiting the signal amplitude within three specific threshold levels. In pitch extraction implementation, we apply this clipping method to speech signals to eliminate noise and interference, resulting in clearer and more stable pitch period detection.

By combining the traditional autocorrelation method with three-level center clipping preprocessing, we effectively extract pitch periods from speech signals, providing a reliable foundation for subsequent speech analysis and recognition tasks. The algorithm implementation typically involves calculating short-term autocorrelation functions after applying center clipping thresholds, followed by peak detection to identify the fundamental period.