MATLAB Code Implementation for Voice Activity Detection Using Cepstrum Method

The voice activity detection program described here presents a highly effective approach for speech signal endpoint detection using cepstral analysis methodology. This technique has demonstrated excellent performance in practical applications. Cepstral analysis serves as a sophisticated mathematical technique specifically designed for speech signal analysis and processing. In implementation, the algorithm typically involves several key steps: first, the input speech signal undergoes preprocessing including framing and windowing operations. The cepstral coefficients are then computed through Fast Fourier Transform (FFT) operations and logarithmic transformations. The core detection mechanism involves analyzing these cepstral features to distinguish between speech and non-speech segments. By applying cepstral analysis, the system can accurately identify the precise starting and ending points of speech signals, which significantly enhances the accuracy and effectiveness of subsequent voice processing tasks. The MATLAB implementation likely includes functions for feature extraction using cepstral coefficients, threshold-based decision algorithms, and post-processing techniques to refine detection boundaries. This method proves particularly valuable in applications such as speech recognition systems, VoIP communications, and audio segmentation tasks where precise endpoint detection is crucial for optimal performance.