MATLAB-based Endpoint Detection Program Using Energy and Zero-Crossing Rate

In MATLAB, we can implement an endpoint detection program based on energy and zero-crossing rate analysis. This program enables more precise identification of audio signal endpoints, facilitating subsequent signal processing and analysis tasks. The energy and zero-crossing rate based endpoint detection method represents a widely-used signal processing technique that determines signal start and end points by analyzing both the energy distribution and zero-crossing patterns of audio signals. Key implementation aspects include: - Calculating short-term energy using frame-based processing with windowing functions (e.g., Hamming window) - Computing zero-crossing rates to capture frequency characteristics and noise patterns - Applying dual-threshold detection logic combining both energy and ZCR metrics - Implementing adaptive thresholding to handle varying audio conditions This approach offers high accuracy and computational efficiency, making it particularly suitable for real-time audio processing applications. The algorithm typically involves segmenting the audio signal into frames, computing energy and ZCR values for each frame, and applying threshold comparisons to identify speech segments while filtering out silence or background noise. For audio signal processing and analysis requirements, employing an energy and zero-crossing rate based endpoint detection program provides an effective solution that balances performance with computational overhead. The MATLAB implementation can leverage built-in functions for signal framing, statistical calculations, and threshold optimization to create robust endpoint detection systems.