噪声环境 Resources

Showing items tagged with "噪声环境"

This paper presents an effective implementation of Gaussian Mixture Models (GMM), a classic speaker recognition algorithm, using the Expectation-Maximization (EM) algorithm. The study primarily simulates GMM's noise robustness performance under various acoustic environments, yielding valuable insights for practical applications. Key implementation aspects include parameter initialization strategies and convergence criteria for the EM iteration process.

MATLAB 271 views Tagged
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Implementation of voice endpoint detection algorithm in MATLAB for accurate speech boundary identification. Provides insights for developing robust VAD systems in noisy environments with code-level implementation details.

MATLAB 254 views Tagged
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MATLAB-based analysis of Amplitude Modulation (AM), Frequency Modulation (FM), and Phase Modulation (PM) signals using second-order cyclostationary spectral analysis under various noise environments including white Gaussian noise, colored noise, non-Gaussian noise, and sinusoidal interference. Users can modify signal parameters and noise conditions within the MATLAB code to analyze corresponding results.

MATLAB 244 views Tagged
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The hmm files implement Hidden Markov Model (HMM) algorithm for speech recognition under noisy conditions. Key components include: vad.m for endpoint detection using energy-based thresholding; mfcc.m for Mel-Frequency Cepstral Coefficients extraction with filter bank processing; pdf.m computing Gaussian probability density output for observation vectors; mixture.m calculating state output probabilities through Gaussian mixture modeling; getparam.m deriving forward/backward probabilities and scaling coefficients; viterbi.m implementing Viterbi algorithm for optimal path decoding; baum.m executing Baum-Welch algorithm for parameter re-estimation; inithmm.m initializing HMM parameters; train.m handling model training procedures.

MATLAB 279 views Tagged
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