In this paper identification of laryngeal disorders using cepstral parameters of human voice is investigated. Mel-frequency cepstral coefficients (MFCC), extracted from audio recordings, are further approximated, using 3 strategies: sampling, averaging, and estimation. SVM and LS-SVM categorize preprocessed data into normal, nodular, and diffuse classes. Since it is a three-class problem, vario...

New refinement schemes for voice conversion are proposed in this paper. We take mel-frequency cepstral coefficients (MFCC) as the basic feature and adopt cepstral mean subtraction to compensate the channel effects. We propose S/U/V (Silence/Unvoiced/Voiced) decision rule such that two sets of codebooks are used to capture the difference between unvoiced and voiced segments of the source speaker...

We examined variants of MFCC and PLP cepstral parameterisations in the context of large vocabulary continuous speech recognition under di erent acoustical environmental conditions: Compared to MFCC, mel-frequency PLP uses a cubic root intensity-toloudness law, and an LPC analysis is applied to the mel-warped spectrum. In LPC-smoothed MFCC, the only di erence to MFCC is the additional LPC smooth...

The performance of traditional mel-frequency cepstral coefficients (MFCC) speech feature extraction method decreases drastically in the complex noisy environment. To improve the performance and robustness of speech recognition system, which is based on spectral envelope estimation method, the minimum distortionless response spectrum MVDR-MFCC (Minimum Variance Distortionless Response-MFCC) feat...

Speaker recognition is the process of recognizing the speaker based on characteristics such as pitch, tone in the speech wave.Background noise influences the overall efficiency of speaker recognition system and is still considered as one of the most challenging issue in Speaker Recognition System (SRS). Support Vector Machine (SVM) and Hidden Markov Model (HMM) are widely used techniques for sp...

Automatic musical genre classification is very useful for many musical applications. In this paper, the features of instrument distribution and instrument-based notes are proposed to represent the high-level characteristics of music. Experimental results show that the proposed features have a good performance in musical genre classification. Comparison between our proposed features with the com...

State-of-the-art automatic speaker recognition systems use mel-frequency cepstral coefficients (MFCC) features to describe the spectral properties of speakers. In forensic phonetics, the long-term average spectrum (LTAS) has been used for the same purpose. LTAS provides an intuitive graphical representation which can be used to visualize and quantify speaker differences. However, few studies ha...

In this paper, a voiced-unvoiced measure is used as acoustic feature for continuous speech recognition. The voiced-unvoiced measure was combined with the standard Mel Frequency Cepstral Coefficients (MFCC) using linear discriminant analysis (LDA) to choose the most relevant features. Experiments were performed on the SieTill (German digit strings recorded over telephone line) and on the SPINE (...

Electroencephalography (EEG) is a tool that allows us to analyze brain activity with high temporal resolution. These measures, combined deep learning and digital signal processing, are widely used in neurological disorder detection emotion mental recognition. In this paper, new method for recognition presented: instantaneous frequency, spectral entropy Mel-frequency cepstral coefficients (MFCC)...

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...