This paper proposes a Mel Frequency Cepstral Coefficient (MFCC) based hybrid algorithm for motor imagery classification of Electroencephalogram (EEG) signal for Brain Computer Interface (BCI). The proposed hybrid algorithm contains MFCC with Hjorth Parameter. Regression coefficient method was used for eye artifacts cancellation. The feature extraction method based on the difference of the diffe...

In this work a multilingual speaker identification system is proposed. The feature extraction techniques employed in the system extract Mel frequency cepstral coefficient (MFCC), delta mel frequency cepstral coefficient (DMFCC) and format frequency. The feature selection is done using hybrid model of particle swarm optimizatiom (PSO) and Genetic Algorithm (GA). We have used Back Propagation (BP...

Currently, one of the most widely used distance measures in speech and speaker recognition is the Euclidean distance between mel frequency cepstral coefficients (MFCC). MFCCs are based on filter bank algorithm whose filters are equally spaced on a perceptually motivated mel frequency scale. The value of mel cepstral vector, as well as the properties of the corresponding cepstral distance, are d...

This paper motivates the use of Dynamic Mel-Frequency Cepstral Coefficient (DMFCC) feature and combination of DMFCC and MFCC features for robust language and text-independent speaker identification. MFCC feature, modeled on the human auditory system has been the widely used feature for speaker recognition because of its less vulnerability to noise perturbation and little session variability. Bu...

In this paper our main aim to provide the difference between cepstral and non-cepstral feature extraction techniques. Here we try to cover-up most of the comparative features of Mel Frequency Cepstral Coefficient and prosodic features. In speaker recognition, there are two type of techniques are available for feature extraction: Short-term features i.e. Mel Frequency Cepstral Coefficient (MFCC)...

Speaker recognition is one of the most essential tasks in the signal processing which identifies a person from characteristics of voices . In this paper we accomplish speaker recognition using Mel-frequency Cepstral Coefficient (MFCC) with Weighted Vector Quantization algorithm. By using MFCC, the feature extraction process is carried out. It is one of the nonlinear cepstral coefficient functio...

The study performs feature extraction for isolated word recognition using Mel-Frequency Cepstral Coefficient (MFCC) for Gujarati language. It explains feature extraction methods MFCC and Linear Predictive Coding (LPC) in brief. The paper compares the performances of MFCC and LPC features under Vector Quantization (VQ) method. The dataset comprising of males and females voices were trained and t...

Nowadays the electronic gadgets have been updated to store large amount of music information. It is necessary to have an efficient retrieval system to choose the required data. The important task in audio retrieval system is feature extraction. In the feature extraction stage, the feature which gives relevant information about music has to be extracted. In this paper, various Mel based feature ...

A K-Nearest Neighbour Algorithm involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform Speech signal feature extraction for the task of speaker accent recognition. Mel-Frequency Cepstral Coefficient is effectively used to perform the feature extraction of the input signal. For each input signal the mean of the MFCC matrix is used for pattern recognition .The K-nearest neig...