Explore MATLAB source code curated for "语音信号处理" with clean implementations, documentation, and examples.
The enframe function and other essential utilities for MATLAB-based speech signal processing are not included in the standard MATLAB installation and require downloading specialized toolboxes. These toolboxes, available from official sources, can be directly extracted and installed. Installation procedures are widely documented online and easily accessible through search.
This repository provides MATLAB toolbox source code for speech signal processing, offering excellent reference value with implementation examples covering fundamental operations and advanced algorithms including voice activity detection, feature extraction, and spectral analysis techniques.
Speech signal processing and speech recognition generally involve preprocessing, feature extraction, vectorization, and matching calculation stages. This guide explains how to implement filtering, framing, windowing, and endpoint detection on speech signals using MATLAB, with specific code examples and algorithm descriptions. Note: The Voicebox toolbox (available online) provides essential functions for comprehensive speech processing implementations.
Record your own voice signal and sample the recorded signal; plot the time-domain waveform and spectrogram of the sampled voice signal; design a filter using the window function method and bilinear transform based on specified filter performance requirements, and plot the filter's frequency response; apply the designed filter to process the acquired signal, plot the filtered signal's time-domain waveform and spectrum, compare pre- and post-filtering signals, and analyze signal changes; playback the voice signal; finally, design a signal processing system interface with integrated functions.
Linear Predictive Coding (LPC) is one of the most effective analysis methods in speech signal processing, enabling identification of pitch periods or formants through LPC spectrum and LPCC spectrum analysis combined with LPC detection methods. MATLAB implementation demonstrates simplicity in programming with excellent performance outcomes, utilizing key functions like lpc() for coefficient estimation and freqz() for spectral analysis.
This implementation provides a comprehensive speech signal processing solution featuring DTW algorithm alongside endpoint detection, MFCC feature extraction, and dynamic time warping components, ready for direct deployment with robust code architecture.
Implementation of FIR low-pass, band-pass, and high-pass filters optimized for speech frequency bands, including code-based design methodologies for voice signal processing applications.
Endpoint detection effectively processes speech signals with excellent detection performance, including implementation of key algorithms for speech onset/offset identification
MATLAB program for speech synthesis in speech signal processing, capable of extracting short-term energy and short-term zero-crossing rate from audio files. The implementation includes frame-based audio processing using windowing techniques and feature calculation algorithms.
This archive contains comprehensive voice modulation code implementations accompanied by audio samples for speech signal processing applications.