Pitch Period Detection in Speech Signals Using AMDF Method

This implementation utilizes MATLAB programming language to detect pitch periods in speech signals through the Average Magnitude Difference Function (AMDF) method. AMDF is a widely-used speech processing technique that determines pitch periods by computing the autocorrelation-like function of speech signals. The algorithm works by calculating the average magnitude differences between a speech signal and its delayed versions, where the delay corresponding to the minimum AMDF value typically indicates the pitch period. In the field of speech signal processing, pitch period detection represents a crucial task. By identifying pitch periods, we can extract fundamental frequency information and rhythmic patterns from speech signals, which is essential for applications such as speech recognition, speech synthesis, and speech coding. The AMDF method effectively analyzes periodic characteristics of speech signals by identifying regular patterns in vocal cord vibrations. Using MATLAB for pitch period detection offers significant advantages. MATLAB provides comprehensive speech processing tools and built-in functions that simplify and optimize the implementation process. Key functions like buffer(), xcorr(), and findpeaks() can be employed to segment speech signals, compute difference functions, and locate pitch peaks efficiently. The AMDF implementation typically involves frame-based processing where each speech frame undergoes difference calculation, valley detection, and period estimation. In summary, combining MATLAB programming with the AMDF method provides an effective approach for pitch period detection in speech signals. This methodology enables deeper understanding and processing of speech characteristics through systematic algorithm implementation and signal analysis techniques. The code implementation typically includes steps for signal preprocessing, frame blocking, AMDF calculation, peak detection, and post-processing for smooth pitch contours.