Calculating Short-Term Energy and Zero-Crossing Rate for Speech Files

First, we will read the speech file and perform frame splitting and windowing, typically using overlapping frames with Hamming or Hanning windows to reduce spectral leakage. Next, we will calculate the short-term energy (measuring amplitude variations within frames) and zero-crossing rate (indicating frequency characteristics) of the speech file. These steps, implementable through digital signal processing libraries like NumPy or MATLAB, can be used for developing and researching music humming retrieval systems. Furthermore, we can explore how to utilize these acoustic features with machine learning algorithms (e.g., SVM or DTW) to enhance system accuracy and performance. We can experiment with different signal processing techniques (such as MFCC extraction or noise reduction) and analyze results to gain deeper insights. Through these efforts, we can continuously improve and optimize music humming retrieval systems to meet diverse user needs and deliver enhanced user experiences via iterative algorithm refinement.