MATLAB-Based Endpoint Detection Using Energy and Zero-Crossing Rate

In speech signal processing, endpoint detection is a crucial step for identifying the start and end points of speech segments. The energy and zero-crossing rate based endpoint detection method is a classical and efficient technique commonly used to eliminate silence or noise components and extract valid speech regions.

### Core Principles Short-term Energy Analysis: The short-term energy of speech signals reflects intensity variations. By computing the squared sum or absolute value sum for each speech frame, speech segments (high energy) can be distinguished from non-speech segments (low energy). In MATLAB implementation, this involves using frame-by-frame processing with functions like `buffer` for segmentation and vectorized operations for energy calculation. Zero-Crossing Rate Analysis: Zero-crossing rate indicates how frequently a signal crosses zero. Unvoiced sounds and noise typically exhibit higher zero-crossing rates, while voiced sounds and silence show lower rates. Combined with energy features, this enables more accurate endpoint detection. Code implementation requires counting sign changes between consecutive samples using logical operations and differential calculations.

### Implementation Approach Frame Segmentation: Divide the speech signal into short-time frames (typically 20-30ms) using overlapping window techniques (e.g., Hamming window) to compute energy and zero-crossing rate per frame. MATLAB code would involve setting frame size, overlap percentage, and applying window functions before feature extraction. Dual-Threshold Method: Set thresholds for both energy and zero-crossing rate. Initial detection identifies candidate segments, followed by backtracking optimization to refine speech start/end points. Algorithm implementation requires logical comparisons with thresholds and state machine logic for segment validation. Smoothing Processing: Apply morphological operations or moving average filters to detection results to prevent false triggers caused by transient noises. MATLAB's `medfilt1` or convolution-based smoothing can be employed here.

### Extended Applications This method is not limited to speech endpoint detection but also applicable to other audio segmentation tasks like musical note detection or environmental sound recognition. Detection accuracy can be further improved by adjusting thresholds or incorporating additional features such as spectral entropy. The modular MATLAB implementation allows easy integration with other feature extraction techniques from the Signal Processing Toolbox.