Endpoint Detection Using Sub-band Spectral Entropy

In the field of signal processing, endpoint detection represents a crucial task. A common approach involves utilizing sub-band spectral entropy for endpoint detection. This method partitions the original signal's spectral entropy into multiple sub-bands, followed by individual detection for each sub-band. The implementation typically involves calculating the spectral entropy through Fast Fourier Transform (FFT) and dividing the frequency spectrum into predefined sub-bands using frequency domain segmentation algorithms. This approach not only enhances detection accuracy but also provides better insights into the signal's frequency characteristics. Key functions in this process include entropy calculation for each frequency bin and threshold-based decision making for endpoint identification. Research indicates that endpoint detection using sub-band spectral entropy yields excellent performance, leading to its widespread adoption in practical applications such as speech processing and bio-signal analysis.