Mel-frequency Cepstral Coefficients Feature Vectors Research Articles

Data-centric artificial olfactory system based on the eigengraph

Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.

Mel Frequency Cepstral Coefficients based Bacterial Foraging Optimization with DNN-RBF for Speaker Recognition

Objectives: To improve the accuracy and to reduce the time complexity of the Speaker Recognition system using Mel-Frequency Cepstral Coefficients (MFCCs) and Bacterial Foraging optimization (BFO) with DNN –RBF. Method: The MFCCs of each speech sample are derived by pre-processing the audio speech signal. The features are optimized with BFO algorithm. Finally, the probability score for each speaker is generated to identify the speaker. Then the features are classified towards the target speaker using DNN-RBF. For the proposed MBFOB speaker recognition function, the TIMIT read corpus is used. It contains a total of 6300 phrases, 10 phrases each. Findings: the identity of user is validated in the fields of authentication and surveillance for recognition of speaker. By using the audio speech signal, features are extracted. This paper suggests an MBFOB solution based on Mel-frequency Cepstral Coefficients and DNN-RBF with BFO, for the identification of speakers. The speech utterance from the TIMIT data corpus is preprocessed to obtain MFCC feature vectors DNN-RBF is used for the purpose of classifying the speaker and the feature vectors in the output layers are optimized with Bacterial Foraging optimization. Finally, the scores for each speaker are calculated to identify the speaker. Different output metrics like EER, DCF, Cavg and accuracy are used to test the proposed speaker recognition technique. The execution time of this proposed method is found to be lesser than the other existing methods. The experimental findings are contrasted with other current methods and it shows the efficiency of our approach. Novelty: A novel MFCC-based Bacterial Foraging Optimization with Deep Neural Network-Radial Basis Function (DNN-RBF) for identification of exact speaker is proposed in this study. Keywords: BFO; DNN; RBF; Speech processing; speaker recognition; MFCC extraction; deep neural network; and Bacterial foraging optimization; scoring

One Solution of Extension of Mel-Frequency Cepstral Coefficients Feature Vector for Automatic Speaker Recognition

One extension of feature vector for automatic speaker recognition is considered in this paper. The starting feature vector consisted of 18 mel-frequency cepstral coefficients (MFCCs). Extension was done with two additional features derived from the spectrum of the speech signal. The main idea that generated this research is that it is possible to increase the efficiency of automatic speaker recognition by constructing a feature vector which tracks a real perceived spectrum in the observed speech. Additional features are based on the energy maximums in the appropriate frequency ranges of observed speech frames. In experiments, accuracy and equal error rate (EER) are compared in the case when feature vectors contain only 18 MFCCs and in cases when additional features are used. Recognition accuracy increased by around 3%. Values of EER show smaller differentiation but the results show that adding proposed additional features produced a lower decision threshold. These results indicate that tracking of real occurrences in the spectrum of the speech signal leads to more efficient automatic speaker recognizer. Determining features which track real occurrences in the speech spectrum will improve the procedure of automatic speaker recognition and enable avoiding complex models.

SVM Based Classification of Sounds from Musical Instruments using MFCC Features

This paper aims at classifying sounds obtained from different musical instruments. The proposed methodology works by extracting the Mel Frequency Cepstral Coefficients (MFCC) of a given sound signal. The extracted features are considered to be vectors input to a Support Vector Machine (SVM). The SVM classifies the MFCC feature vector of the given sound signal by using a Minimum Distance Classifier (MDC) based classification scheme which operates by calculating the Euclidean distances of the given vector from the representative pattern vectors of the different pattern classes that the SVM has been trained with. The given sound signal is then identified as being a member of the class for which the Euclidean distance is the minimum; and is thus classified.

Non‐intrusive speech quality assessment using multi‐resolution auditory model features for degraded narrowband speech

A multi-resolution framework using auditory perception-based wavelet packet transform is invoked in multi-resolution auditory model (MRAM) and used for non-intrusive objective speech quality estimation. The MRAM provides a detailed time-frequency modelling of the human auditory system compared to earlier models that have been used for non-intrusive speech quality estimation. The objective Mean Opinion Score (MOS) of a degraded narrowband speech utterance has been estimated by Gaussian Mixture Model (GMM) probabilistic approach using MRAM-based feature vector. Additionally, a recent auditory model (Lyons’ auditory model) based features, mel-frequency cepstral coefficients (MFCC), and line spectral frequencies (LSF) features have also been used independently for comparison of the performance of MRAM features. The combination of MFCC and LSF features with MRAM features for non-intrusive speech quality estimation using GMM probabilistic approach has been proposed and investigated. The performance of these feature vectors has been evaluated and compared with ITU-T Recommendation P.563 and a recent published work by computing correlation coefficient and root-mean-square error between the subjective MOS and the estimated objective MOS. It is found that the proposed method that uses a combination of MRAM features, MFCC, and LSF feature vectors for non-intrusive speech quality performs better than both the other algorithms.

Model-Based Feature Enhancement for Reverberant Speech Recognition

In this paper, we present a new technique for automatic speech recognition (ASR) in reverberant environments. Our approach is aimed at the enhancement of the logarithmic Mel power spectrum, which is computed at an intermediate stage to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean square error estimate of the clean LMPSCs is computed by carrying out Bayesian inference. We employ switching linear dynamical models as an a priori model for the dynamics of the clean LMPSCs. Further, we derive a stochastic observation model which relates the clean to the reverberant LMPSCs through a simplified model of the room impulse response (RIR). This model requires only two parameters, namely RIR energy and reverberation time, which can be estimated from the captured microphone signal. The performance of the proposed enhancement technique is studied on the AURORA5 database and compared to that of constrained maximum-likelihood linear regression (CMLLR). It is shown by experimental results that our approach significantly outperforms CMLLR and that up to 80% of the errors caused by the reverberation are recovered. In addition to the fact that the approach is compatible with the standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of moderate computational complexity and suitable for real time applications.

Palmprint recognition based on Mel frequency Cepstral coefficients feature extraction

Palmprint identification is a measurement of palmprint features for recognizing the identity of a user. Palmprint is universal, easy to capture and it does not change much across time. This paper presents an application of Mel frequency Cepstral coefficients (MFCCs) for identification of palmprint. Palmprint feature extraction is based on transforming the palmprint image into one dimensional (1-D) signal and then extracting MFCCs from this signal. Wavelet transform (DWT) of the 1-D palmprint signals are used for extracting additional features to help in the recognition process. The features from MFCCs of this DWT vector are added to the MFCCs feature vector, generated from the original palmprint signal, to form a large feature vector that can be used for palmprint identification. Feature matching is performed in this research using feed forward back propagation error neural network. Experimental results show that the proposed method is robust in the presence of noise.

Emotion Recognition using Mel-Frequency Cepstral Coefficients

In this paper, we propose a new approach to emotion recognition. Prosodic features are currently used in most emotion recognition algorithms. However, emotion recognition algorithms using prosodic features are not sufficiently accurate. Therefore, we focused on the phonetic features of speech for emotion recognition. In particular, we describe the effectiveness of Mel-frequency Cepstral Coefficients (MFCCs) as the feature for emotion recognition. We focus on the precise classification of MFCC feature vectors, rather than their dynamic nature over an utterance. To realize such an approach, the proposed algorithm employs multi-template emotion classification of the analysis frames. Experimental evaluations show that the proposed algorithm produces 66.4% recognition accuracy in speaker-independent emotion recognition experiments for four specific emotions. This recognition accuracy is higher than the accuracy obtained by the conventional prosody-based and MFCC-based emotion recognition algorithms, which confirms the potential of the proposed algorithm.