Mel Frequency Cepstral Coefficient Analysis Research Articles

Support Vector Machine Performance with Optimal Parameters Identification in Recognising Asphyxiated Infant Cry

Detection of asphyxia in infant at an early stage is crucial to reduce the rate of infant morbidity. The information regarding asphyxia can be extracted from infant cry signals using support vector machine (SVM) combined with effective feature selection methods such as principal component analysis (PCA) or orthogonal least square (OLS). The performance of SVM in recognizing infant cry with asphyxia after undergone comprehensive identification of optimal parameters at the feature extraction and classification stages has not been reported. This paper describes the two stages of optimal parameter identification; at Mel-frequency Cepstral coefficient (MFCC) analysis stage, SVM with and without employing the PCA and OLS stages, and the performance of the SVM in recognizing infant cry with asphyxia resulted from all levels of optimal parameters identification. The SVM was first optimized after performing MFCC analysis to find the optimum parameters. Two types of kernels were used, the polynomial and RBF kernels. The subsequent SVM optimizations were conducted after PCA and OLS were employed. In the PCA, the significant features were selected using eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE). The SVM performance was evaluated based on classification accuracy and computation time. The experimental results have shown that the optimized SVM was able to recognize the asphyxiated infant cry with an accuracy of 94.84% and computation time of 1.98s using PCA with EOC and RBF kernel.

Acoustical User Identification Based on MFCC Analysis of Keystrokes

This paper introduces a novel approach of person identification using acoustical monitoring of typing the required word on the monitored keyboard. This experiment was motivated by the idea of COST IC1106 (Integrating Biometrics and Forensics for the Digital Age) partners to acoustically analyse the captured keystroke dynamics database using widely used time-invariant mathematical models tools. The MFCC (Mel-Frequency Cepstral Coefficients) and HMM (Hidden Markov Models) was introduced in this experiment, which gives promising results of 99.33% accuracy, when testing 25% of realizations (randomly selected from 100) identifying between 50 users/models. The experiment was repeated for different training/testing configurations and cross-validated, so this first approach could be a good starting point for next research including feature selection algorithms, biometric authentication score normalization, different audio & keyboard setup tests, etc.

Improved MFCC-Based Feature for Robust Speaker Identification

The Mel-frequency cepstral coefficient (MFCC) is the most widely used feature in speech and speaker recognition. However, MFCC is very sensitive to noise interference, which tends to drastically degrade the performance of recognition systems because of the mismatches between training and testing. In this paper, the logarithmic transformation in the standard MFCC analysis is replaced by a combined function to improve the noisy sensitivity. The proposed feature extraction process is also combined with speech enhancement methods, such as spectral subtraction and median-filter to further suppress the noise. Experiments show that the proposed robust MFCC-based feature significantly reduces the recognition error rate over a wide signal-to-noise ratio range.