نتایج جستجو برای: mel frequency cepstral coefficient mfcc
تعداد نتایج: 644930 فیلتر نتایج به سال:
Voice control is an important way of controlling mobile devices; however, using it remains a challenge for dysarthric patients. Currently, there are many approaches, such as automatic speech recognition (ASR) systems, being used to help patients devices. However, the large computation power requirement ASR system increases implementation costs. To alleviate this problem, study proposed convolut...
Automatic detection of emotions has been evaluated using standard Mel-frequency Cepstral Coefficients, MFCCs, and a variant, MFCC-low, that is calculated between 20 and 300 Hz in order to model pitch. Plain pitch features have been used as well. These acoustic features have all been modeled by Gaussian mixture models, GMMs, on the frame level. The method has been tested on two different corpora...
In this paper, hum of a person is used in voice biometric system. In addition, recently proposed feature set, i.e., Variable length Teager Energy Based Mel Frequency Cepstral Coefficients (VTMFCC), is found to capture perceptually meaningful source-like information from hum signal. For person recognition, MFCC gives EER of 13.14% and %ID of 64.96%. A reduction in equal error rate (EER) by 0.2% ...
This paper proposes features based on parametric representation of Fourier phase of speech for speaker verification. Direct computation of Fourier phase suffers from phase wrapping and hence we attempt parametric modelling of phase spectrum using an allpass (AP) filter. The coefficients of the AP filter are estimated by minimizing an entropy based objective function motivated from speech produc...
Automatic voice pathology detection enables objective assessment of pathologies that affect the production mechanism. Detection systems have been developed using traditional pipeline approach (consisting feature extraction part and part) modern deep learning -based end-to-end approach. Due to lack vast amounts training data in study area pathological voice, former is still a valid choice. In ex...
This paper addresses the performance of various statistical data fusion techniques for combining the complementary score information in speaker verification. The complementary verification scores are based on the static and delta cepstral features. Both LPCC (Linear prediction-based cepstral coefficients) and MFCC (mel-frequency cepstral coefficients) are considered in the study. The experiment...
The significance of features derived from complex analytic domain representation of speech, for different applications, is investigated. Frequency domain linear prediction (FDLP) coefficients are derived from analytic magnitude and instantaneous frequency (IF) coefficients are derived from analytic phase of speech signals. Minimal pair ABX (MP-ABX) tasks are used to analyse different features a...
Heart murmurs are sounds made by rapid blood flow in the heart. Abnormal heart can be a sign of serious conditions such as arrhythmia and cardiovascular diseases. Therefore, murmur classification is crucial for early detection conditions. To this end, we study problem training selected convolutional neural network (CNN) models (such VGGNet ResNet) using various signal representations spectrogra...
BACKGROUND Over recent years numerous papers have stressed that production of voice is subjected to the nonlinear processes, which cause aperiodic vibrations of vocal folds. These vibrations cannot always be characterized by means of conventional acoustic parameters, such as measurements of frequency and amplitude perturbations. Thus, special attention has recently been paid to nonlinear acoust...
This paper presents robust feature extraction techniques, called Mel Power Karhunen Loeve Transform Coefficients (MPKC), Mel Power Coefficients (MPC) for an isolated digit recognition. This hybrid method involves Stevens’ Power Law of Hearing and Karhunen Loeve(KL) Transform to improve noise robustness. We have evaluated the proposed methods on a Hidden Markov Model (HMM) based isolated digit r...
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