Dual-Threshold Endpoint Detection Algorithm with MATLAB Implementation

This documentation presents a MATLAB implementation of the dual-threshold endpoint detection algorithm. While the code has been successfully compiled and integrated into a Word document, we believe it warrants more detailed technical explanations. Here, we will elaborate on the algorithm's fundamental principles and practical applications to enhance your understanding of the program.

The dual-threshold endpoint detection algorithm operates by establishing two distinct thresholds: a lower threshold and a higher threshold. These thresholds partition the signal into three distinct regions: signals below the lower threshold, signals between the two thresholds, and signals above the higher threshold. Originally developed for speech signal processing, this algorithm demonstrates versatility in handling various signal types including biomedical signals and environmental sensor data.

The MATLAB implementation showcases practical coding techniques for threshold-based signal segmentation. Key programming components include: threshold initialization using statistical methods like percentile calculations or adaptive level setting, signal region classification through conditional statements and logical indexing, and endpoint identification employing cross-threshold detection logic with hysteresis to prevent false triggers. The code structure typically involves frame-based processing with overlapping windows for improved temporal resolution, incorporating noise floor estimation and signal normalization routines.

While this MATLAB program compiles successfully and integrates well with Word documentation, we can further explore advanced implementation aspects such as dynamic threshold adjustment based on signal-to-noise ratio, multi-channel signal support, and real-time processing capabilities. These enhancements would provide deeper insights into both the algorithm's theoretical foundations and its practical MATLAB implementation.