Emotion Detection Using MFCC and Cepstrum Features

Lalitha, S.; Geyasruti, D.; Ramamurthi, Narayanan; Shravani, M.

doi:10.1016/j.procs.2015.10.020

Cited by 86 publications

(33 citation statements)

References 5 publications

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“…We use the MFCC to extract features from the audio signal. The MFCC is a linear representation of the cosine transforms of a short duration of logarithmic power spectrum of the sound signal on a non-linear scale Mel frequency [20]. It perceives frequency in a logarithmic way, inspired in the behavior of the human ear.…”

Section: Time Featuresmentioning

confidence: 99%

Classifying Heart Sounds Using Images of MFCC and Temporal Features

Nogueira

Ferreira

Jorge

2017

Progress in Artificial Intelligence

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Phonocardiogram signals contain very useful information about the condition of the heart. It is a method of registration of heart sounds, which can be visually represented on a chart. By analyzing these signals, early detections and diagnosis of heart diseases can be done. Intelligent and automated analysis of the phonocardiogram is therefore very important, to determine whether the patient's heart works properly or should be referred to an expert for further evaluation. In this work, we use electrocardiograms and phonocardiograms collected simultaneously, from the Physionet challenge database, and we aim to determine whether a phonocardiogram corresponds to a "normal" or "abnormal" physiological state. The main idea is to translate a 1D phonocardiogram signal into a 2D image that represents temporal and Mel-frequency cepstral coefficients features. To do that, we develop a novel approach that uses both features. First we segment the phonocardiogram signals with an algorithm based on a logistic regression hidden semi-Markov model, which uses the electrocardiogram signals as reference. After that, we extract a group of features from the time and frequency domain (Mel-frequency cepstral coefficients) of the phonocardiogram. Then, we combine these features into a two-dimensional time-frequency heat map representation. Lastly, we run a binary classifier to learn a model that discriminates between normal and abnormal phonocardiogram signals. In the experiments, we study the contribution of temporal and Mel-frequency cepstral coefficients features and evaluate three classification algorithms: Support Vector Machines, Convolutional Neural Network, and Random Forest. The best results are achieved when we map both temporal and Mel-frequency cepstral coefficients features into a 2D image and use the Support Vector Machines with a radial basis function kernel. Indeed, by including both temporal and Mel-frequency cepstral coefficients features, we obtain sligthly better results than the ones reported by the challenge participants, which use large amounts of data and high computational power.

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Section: Time Featuresmentioning

confidence: 99%

Classifying Heart Sounds Using Images of MFCC and Temporal Features

Nogueira

Ferreira

Jorge

2017

Progress in Artificial Intelligence

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“…Cepstrum analysis is widely used in signal processing in many areas. For example to glottal flow estimation [15], identification of damage in civil engineering structures [16], detection of voice disorders [17], detection of emotions [18], estimating the heart rate from arrays of fiber Bragg grating (FBG) sensors [19], heart rate estimation (improve clarity of data from photoplethysmography) [20], and improve the detection quality of micro changes in biological structures [21]. There are also few publications in the literature regarding the reduction of the multipath effect in the hydroacoustic channel during data transmission, such as [22], where authors are using pulse position modulation spread spectrum underwater acoustic communication system using the N-H sequence.…”

Section: Introductionmentioning

confidence: 99%

Reduction of the Multipath Propagation Effect in a Hydroacoustic Channel Using Filtration in Cepstrum

Czapiewska

Łuksza

Studański

et al. 2020

Sensors

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During data transmission in a hydroacoustic channel, one of the problems is the multipath propagation effect, which leads to a decrease in the transmission parameters and sometimes completely prevents it. Therefore, we have attempted to develop a method, which is based on a recorded hydroacoustic signal, that allows us to recreate the original (generated) signal by eliminating the multipath effect. In our method, we use cepstral analysis to eliminate replicas of the generated signal. The method has been tested in simulation and during measurements in a real environment. Additionally, the influence of the method on data transmission in the hydroacoustic channel was tested. The obtained results confirmed the usefulness of the application of the developed method and improved the quality of data transmission by reducing the multipath propagation effect.

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“…A wide research has been done on speech emotion recognition to improve the human computer interaction [6]. Most researches are done using Berlin Emotional speech database comprising different emotions [7]. A detailed review of the databases comprising 32 emotional speech databases including English, German, Spanish, Dutch, Russian, Sweden and Chinese are presented in [8].…”

Section: Introductionmentioning

confidence: 99%

An epitomization of stress recognition from speech signal

Narayanan

Lalitha

Gupta

2018

IJET

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The Detection of stress from speech signal is gaining large attention recently. The emergence of new methods and techniques for feature extraction and classification paved the way to different solutions to detect different stress conditions using human speech and led to an in-crease in the accuracy of stress recognition. A large number of parameters are proposed for the characterization of stress in speech. Similarly numerous classifiers and machine learning algorithms are investigated for stress classification and regression. In this treatise, a recital on the commonly used databases, stress conditions, different feature extraction methods and classifiers along with some of the statistical measures as well as compensation techniques for stress detection are presented in this article. After thorough illustration of existing methodology for the task, future prospects for the work are elaborated.

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Emotion Detection Using MFCC and Cepstrum Features

Cited by 86 publications

References 5 publications

Classifying Heart Sounds Using Images of MFCC and Temporal Features

Classifying Heart Sounds Using Images of MFCC and Temporal Features

Reduction of the Multipath Propagation Effect in a Hydroacoustic Channel Using Filtration in Cepstrum

An epitomization of stress recognition from speech signal

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