Entropy-Based Endpoint Detection Algorithm

The entropy-based endpoint detection algorithm represents a more precise speech signal processing method compared to traditional approaches. Conventional endpoint detection typically relies on short-term energy or zero-crossing rate, which often fails in low signal-to-noise ratio (SNR) environments. In contrast, the entropy-based method leverages statistical properties of speech signals to more reliably identify the start and end points of speech segments.

The core algorithm concept involves calculating the information entropy for each signal frame. Since speech segments contain rich information, their entropy values are typically higher than those of background noise. By establishing appropriate entropy thresholds, the system can distinguish between speech and non-speech regions. Key implementation steps include frame segmentation, entropy calculation per frame, smoothing processing, and threshold comparison. In code implementation, this typically involves using sliding window techniques for framing and Shannon entropy computation for probability distributions derived from signal amplitudes or spectral coefficients.

The primary advantage of entropy endpoint detection lies in its robustness to noise, making it particularly suitable for complex environmental scenarios. However, it requires slightly higher computational resources, and threshold selection significantly impacts the results. In practical applications, it's often combined with other features (such as spectral variations) to further enhance accuracy. Programmers can optimize performance by implementing efficient entropy calculation algorithms and adaptive thresholding techniques based on real-time signal characteristics.