Short-Time Zero-Crossing Rate Implementation for Voice Activity Detection
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In voice activity detection, we implement a program based on short-time zero-crossing rate to enhance endpoint detection accuracy. This algorithm effectively identifies speech segments within audio signals by analyzing the frequency of signal sign changes across short time frames. The implementation involves calculating zero-crossing counts within sliding windows, typically using frame sizes of 20-30ms with 50% overlap. Through this method, we achieve improved voice activity detection performance by distinguishing speech from silence based on spectral characteristics, significantly boosting recognition rates. The core function computes zero-crossing rates using absolute value comparisons and threshold-based decision logic for reliable endpoint identification.
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