Explore MATLAB source code curated for "短时能量" with clean implementations, documentation, and examples.
Short-term analysis of speech signals includes key components such as frame splitting, short-term energy, short-term average magnitude, short-term zero-crossing rate, short-term autocorrelation function, short-term magnitude difference, cepstrum, complex cepstrum, LPC coefficients, and LPC spectral estimation. These fundamental programs assigned by my supervisor after securing postgraduate admission ensure absolute quality through robust implementation.
MATLAB-based implementation of speech signal time-domain feature extraction including frame splitting, zero-crossing rate, short-term energy, and spectrogram analysis with code examples and algorithm explanations.
This analysis explores key aspects of speech signal processing using MATLAB, including short-time energy computation, endpoint detection algorithms, and power spectrum analysis with short-time Fourier transform characteristics. The study provides practical code implementation approaches for each technique.
Perform preprocessing on a speech signal, including windowing, calculating zero-crossing rate, short-term energy, and extracting various speech signal feature parameters. This process involves time-domain analysis techniques commonly implemented using signal processing libraries.
This is an initial study on endpoint detection utilizing short-term energy and spectral entropy features in speech signal processing
Implementation of voice activity detection through short-term energy analysis and spectral entropy measurement with algorithmic enhancements.
MATLAB code implementation for basic endpoint detection using dual-threshold approach with short-term energy and zero-crossing rate analysis