MATLAB Calculation and Visualization of Short-Term Energy Curve or Short-Term Average Amplitude Curve

To accomplish this task as described above, follow these implementation steps:

1. Record a speech signal using recording equipment or applications, ensuring clear audio capture for processing.

2. Convert the sampling rate to 44100Hz using MATLAB's resample function or audiowrite with specified parameters to standardize the signal for subsequent analysis.

3. Implement MATLAB code to calculate and plot either the short-term energy curve or short-term average amplitude curve of your recording. The algorithm involves frame-based processing where energy is computed as the sum of squared samples per frame, while average amplitude uses the absolute value mean. Use MATLAB's plotting functions like plot() or stem() for visualization.

4. Configure processing parameters for optimal accuracy: set frame length to 20ms (882 samples at 44100Hz), frame shift to 10ms (441 samples), and apply rectangular window function using windowing techniques. This framing approach ensures proper temporal analysis while maintaining signal characteristics.

These steps will yield high-quality short-term feature curves that reveal temporal characteristics of your audio signal. For enhanced results, optimize recording settings in your equipment/application to improve signal quality. Remember that patience and attention to detail are crucial, as this task requires multiple processing stages, computational steps, and visualization operations.