Spectral Entropy-Based Endpoint Detection

In the field of speech signal processing, endpoint detection serves as a fundamental and critical technique aimed at accurately identifying the start and end positions of speech segments within continuous audio streams. Spectral entropy, as an information theory-based endpoint detection method, is widely adopted in practical applications due to its computational efficiency and relative robustness against noise.

The core concept of spectral entropy endpoint detection involves distinguishing speech segments from non-speech segments by calculating the spectral entropy values of speech signals. The implementation workflow typically includes: Performing frame-based processing on audio signals, with standard frame lengths of 20-30ms and frame shifts of 10ms; Applying Fast Fourier Transform (FFT) to each frame to obtain frequency spectrum information; Normalizing the spectral energy to form a probability distribution, then computing spectral entropy values using information entropy formulas; Implementing threshold-based decision logic to classify frames as speech or non-speech segments.

During implementation, complementary features like short-term energy can be integrated to enhance detection robustness. In high-noise environments, preprocessing techniques such as spectral subtraction or Wiener filtering may be applied for noise reduction. Key programming considerations include optimal FFT window selection (e.g., Hamming window) and dynamic threshold adaptation algorithms.

Adopting spectral entropy method for endpoint detection in graduation projects provides excellent learning opportunities in both classical speech processing techniques and practical parameter optimization (e.g., adjusting frame length, entropy thresholds). This approach offers balanced theoretical and practical integration. Future extensions could explore comparative studies with advanced features like Mel-Frequency Cepstral Coefficients (MFCC) or deep learning-based detection models.