Explore MATLAB source code curated for "语音信号" with clean implementations, documentation, and examples.
Blind source separation of speech signals provides an excellent separation procedure that operates without prior knowledge of the signals, typically implementing algorithms like Independent Component Analysis (ICA) or Non-negative Matrix Factorization (NMF).
Algorithms for Extracting Formants from Speech Signals with Code Implementation Approaches
Speech signal denoising using wavelet analysis with voiced/unvoiced discrimination, implemented through multi-resolution signal processing and threshold-based noise reduction techniques.
Design a digital audio effects processor capable of performing various audio effect processing on speech signals. Requirements: (1) Input speech signal source must be collected from real-world environments; (2) Implement at least 3 audio processing functions: 1. Voice recognition for 5 project team members; 2. Noise reduction for at least 3 types of noise in real speech samples exceeding 10 seconds; 3. Equalizer functionality; (3) Control via human-machine interface with audio output through speakers/headphones.
MATLAB source code for speech signal subband coding implementation with detailed algorithm explanations and function descriptions
This implementation performs noise addition to speech signals followed by processing using low-pass, band-pass, and high-pass filters. The program runs perfectly, but requires careful attention to audio file path configuration to avoid runtime errors! Includes code descriptions for signal processing workflows and filter implementations.
An isolated word recognition system based on the Dynamic Time Warping (DTW) algorithm, implemented using MATLAB programming. The system performs endpoint detection on speech signals to extract effective speech segments from raw audio data, enabling accurate isolated word recognition through time-series pattern matching.
A comprehensive MATLAB GUI design example demonstrating audio signal recording, sampling, filtering, reconstruction, and spectral characteristic analysis with detailed code implementation and algorithm explanations.
Extraction of speech signal features, including methods for obtaining Mel Frequency Cepstral Coefficients (MFCC), principles of linear prediction for speech signals, and derivation of LPC features with code implementation insights
Frequency-invariant beamforming (FIB) plays a crucial role in distortion-free acquisition and processing of speech signals using microphone arrays. While classical FIB design methods typically operate under far-field plane wave assumptions, this paper introduces a novel approach based on the near-field spherical wave model. We integrate the spatial response variation (SRV) function into near-field Linear Constrained Minimum Variance (LCMV) broadband beamforming, formulating the spatial response as an optimization constraint and deriving a closed-form solution via Lagrange multipliers. The implementation involves calculating frequency-invariant weight vectors through matrix inversion operations, with key computational steps including covariance matrix estimation and constraint matrix construction. This method enhances robustness against environmental interference compared to conventional approaches.