Computing Short-Term Energy of Audio Signals

In audio signal processing, computing short-term energy and zero-crossing rate represents fundamental techniques. Short-term energy refers to the sum of squared signal values within brief time intervals, commonly employed for detecting signal intensity and power levels. Zero-crossing rate indicates the frequency at which the signal crosses the zero-axis within short time windows, typically used for analyzing signal frequency characteristics and pitch information. By calculating both short-term energy and zero-crossing rate, we can implement voice activity detection (VAD) to identify speech start and end points. This process involves framing the audio signal into overlapping segments, applying window functions (like Hamming window), and computing energy/zcr features for each frame. Threshold-based algorithms or machine learning classifiers can then distinguish speech from non-speech segments. These techniques are crucial for speech recognition systems, speech synthesis applications, and various voice-enabled technologies, with implementations often involving NumPy/SciPy in Python or specialized audio processing toolboxes.