Endpoint Detection Algorithm

Endpoint detection algorithms play a crucial role in speech signal processing by accurately identifying the start and end points of speech segments. This technology primarily relies on analyzing multidimensional features of audio signals to achieve effective detection. In code implementation, this typically involves frame-based processing where the audio signal is divided into short overlapping frames (commonly 20-30ms duration with 10ms overlap) for feature extraction.

Multi-band analysis methods decompose the input signal into different frequency bands for processing, using energy variations across sub-bands to comprehensively determine speech segment locations. This approach offers greater robustness compared to single-band analysis and better handles various environmental noises. Implementation typically involves applying band-pass filters or wavelet transforms to separate frequency components, followed by energy calculation in each sub-band using functions like numpy's FFT or scipy.signal.filter.

Spectral entropy serves as an important detection feature that reflects the energy distribution of signals in the frequency domain. Speech segments typically exhibit significantly lower spectral entropy values than noise segments, providing reliable basis for endpoint determination. The computational process involves converting the power spectrum into a probability distribution, then applying the information entropy formula: H = -∑(p_i * log(p_i)), where p_i represents the normalized power spectrum component.

Energy analysis constitutes the most fundamental detection method, capturing intensity variations in speech signals through short-time energy calculations. When combined with time-domain features like zero-crossing rate, it effectively distinguishes between unvoiced and voiced segments. Practical implementations often use logarithmic energy to enhance detection sensitivity, calculated as log(1 + ∑(x_i²)) where x_i represents frame samples, preventing numerical issues with near-zero values.

While these techniques can be used individually, feature fusion is more common in modern implementations. By weighted combination of multiple feature decision results, detection accuracy significantly improves, particularly in low signal-to-noise ratio environments. Contemporary endpoint detection systems often incorporate machine learning methods (such as SVM or neural networks) to automatically optimize feature weights through training on labeled datasets, using scikit-learn or TensorFlow frameworks for implementation.