Pitch Period Extraction Algorithms in Speech Signal Processing

This article presents English literature and technical articles related to pitch period extraction algorithms in speech signal processing. We will comprehensively explore the underlying principles, application domains, and latest research advancements of these algorithms. Key implementation approaches include autocorrelation-based methods, which detect periodicity by analyzing signal self-similarity through MATLAB's xcorr function, and cepstral analysis techniques that utilize inverse Fourier transforms to separate excitation and vocal tract components. Additionally, we will analyze the advantages and limitations of these algorithms, such as the robustness of wavelet-transform based methods against noise interference versus their computational complexity. The article also proposes potential improvement directions, including machine learning-enhanced pitch tracking using LSTM networks for continuous speech processing. Through detailed technical exposition, readers will gain comprehensive understanding of pitch period extraction methodologies and acquire valuable references for related research and practical applications.