Comparison of different implementations of MFCC

SpeakerSense: Energy Efficient Unobtrusive Speaker Identification on Mobile Phones

“…The speaker identification stage is based on a Gaussian Mixture Model (GMM) classifier [13] with some modifications to improve robustness and computation efficiency for use on the mobile phone. We use as our feature vector pitch [11] and the Mel-frequency cepstral coefficients (MFCCs) [20] computed for each admitted frame. SpeakerSense computes 20-dimensional MFCCs, and then ignores the first coefficient, which represents the energy of the frame, and instead focuses on the spectral shape, represented by the 2 nd through 20 th coefficients.…”

“…Similar to both of these systems, our goal is not to design new speaker identification algorithms. Instead we leverage well-established techniques such as the MFCCs feature set [20], pitch tracking [11], and GMM classifiers [e.g., 13,14], which have been proven effective for speaker identification. Our focus is on adapting these techniques to a mobile platform and addressing challenges that arise when using speaker identification on energy constrained mobile phones.…”

iCub Visual Memory Inspector: Visualising the iCub’s Thoughts

“…As such there is currently work being carried out on the use of Mel-Frequency Ceptral Coefficients (MFCC) [10] paired with a Gaussian Mixture Model (GMM) to construct classification vectors for training with SAM.…”

Polyphonic Pitch Estimation and Instrument Identification by Joint Modeling of Sustained and Attack Sounds

“…The proposed algorithm is compared with conventional 12-dimensional Mel-Frequency Cepstral Coefficients (MFCCs) [33], with the source-filter model of harmonic partials in [2] and with the harmonic features proposed in our previous work [26]. MFCCs are extracted from the power spectrum of each note and classified by SVM.…”