Speaker Gender Identification Using AMDF-Based Pitch Period Extraction

AMDF (Average Magnitude Difference Function) represents a classical pitch period extraction algorithm particularly suitable for speech signal analysis. This method identifies periodic characteristics by calculating the average magnitude difference between the original signal and its delayed version, thereby obtaining the pitch period. In code implementation, the AMDF function typically involves sliding a window through the signal and computing differences between overlapping segments, with the minimum difference indicating the pitch period.

For speaker gender identification tasks, the AMDF approach primarily involves two key steps: First, extract pitch periods from speech signals using the AMDF algorithm, where the algorithm implementation would include frame blocking, windowing, and difference computation. Second, perform gender classification based on statistical differences between male and female fundamental frequencies (typically 85-180Hz for males and 165-255Hz for females). This classification can be implemented through threshold-based decision rules or machine learning classifiers trained on pitch frequency distributions.

The advantage of AMDF lies in its straightforward implementation and sensitivity to periodic signals. However, developers should note its weaker robustness against noise and aperiodic components. Practical applications often incorporate preprocessing techniques (such as band-pass filtering to remove non-voice frequencies) and postprocessing methods (like dynamic programming for pitch trajectory smoothing) to enhance accuracy. This method demonstrates strong extensibility and can be combined with other acoustic features (such as formant frequencies) using feature fusion techniques to optimize classification performance.