Concise and Practical Pitch Period Extraction Program

We have developed a concise and practical pitch period extraction program that implements a hybrid method combining autocorrelation function (ACF) and average magnitude difference function (AMDF). This dual-approach methodology significantly improves pitch extraction accuracy and reliability by leveraging complementary signal processing techniques. The implementation first computes the ACF to identify periodic waveform patterns through peak detection algorithms, which effectively captures fundamental frequency components. Simultaneously, the AMDF calculation handles noise robustness and signal distortion by analyzing amplitude variations across different lag intervals. The program intelligently weights and combines results from both functions using a decision fusion algorithm to determine optimal pitch periods. Key implementation features include: - Frame-based processing with Hamming window application for signal segmentation - Dynamic threshold adaptation for peak picking in both ACF and AMDF domains - Cross-validation mechanism between the two methods to eliminate octave errors Our program demonstrates excellent extensibility and flexibility, supporting various speech processing applications through modular code architecture. The object-oriented design allows easy integration of additional pitch detection algorithms and parameter customization for different audio characteristics. In summary, this program offers high practical value and potential application significance in pitch period extraction, featuring robust performance across diverse speech signals and noise conditions.