We divided MFCC into two parts, low-MFCC and middle-MFCC, by the bandwidth. Additionally, we made an identification experiments on those MFCCs. Using those ...
We divided. MFCC into two parts, low-MFCC and middle-MFCC, by the bandwidth. Additionally, we made an identification experiments on those MFCCs. Using those ...
Oct 22, 2024 · Recent research indicates that a new speaker feature, gammatone frequency cepstral coefficients (GFCC), exhibits superior noise robustness to ...
Apr 17, 2021 · 2. B. Robustness of Speaker Identification Systems. Practical speaker recognition systems are often subject to noise or distortions within the ...
Fractional MFCC based feature extraction approach is employed which is robust against the noise as compared to the conventional. MFCC. The joint factor analysis ...
We compared the MMeDuSA's performance with traditional MFCC features and previously proposed noise robust features on retransmitted channel and noise corrupted.
Speaker recognition experiments show that the usage of a UBM in combination with missing data recognition yields substantial im- provements in recognition ...
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Aug 28, 2019 · One popular audio feature extraction method is the Mel-frequency cepstral coefficients (MFCC) which have 39 features.
This example demonstrates a machine learning approach to identify people based on features extracted from recorded speech.
Missing: robust | Show results with:robust
Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model. (GMM) speaker recognition method.