Classification of heart sounds using linear prediction coefficients and mel-frequency cepstral coefficients as acoustic features

2017 ◽  
Author(s):  
Pedro Narvaez ◽  
Katerine Vera ◽  
Nhikolas Bedoya ◽  
Winston S. Percybrooks
Keyword(s):  
Linear Prediction ◽  
Heart Sounds ◽  
Acoustic Features ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients

scholarly journals Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ömer Eskidere ◽  
Ahmet Gürhanlı
Keyword(s):  
Voice Disorder ◽  
Mel Frequency Cepstral Coefficients ◽  
Essential Information ◽  
Audio Processing ◽  
Voice Signal ◽  
Window Technique ◽  
Normal Voice ◽  
Cepstral Coefficients ◽  
Better Than

The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later.

scholarly journals Automatic Content based Classification of Speech Audio using Multiple Instance Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 410-414
Keyword(s):  
Linear Prediction ◽  
Research Problem ◽  
Multiple Instance Learning ◽  
Audio Classification ◽  
Mel Frequency Cepstral Coefficients ◽  
Content Understanding ◽  
Novel Approach ◽  
Active Research ◽  
Perceptual Linear Prediction

Audio content understanding is an active research problem in the area of speech analytics. A novel approach for content-based news audio classification using Multiple Instance Learning (MIL) approach is introduced in this paper. Content-based analysis provides useful information for audio classification as well as segmentation. A key step taken in this direction is to propose a classifier that can predict the category of the input audio sample. There are two types of features used for audio content detection, namely, Perceptual Linear Prediction (PLP) coefficients and Mel-Frequency Cepstral Coefficients (MFCC). Two MIL techniques viz. mi-Graph and mi-SVM are used for classification purpose. The results obtained using these methods are evaluated using different performance matrices. From the experimental results, it is marked that the MIL demonstrates excellent audio classification capability.

scholarly journals A Peak Detection Algorithm for Localization and Classification of Heart Sounds in PCG Signals using K-means Clustering

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Detection Algorithm ◽  
Heart Sounds ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Classifier ◽  
Mel Frequency Cepstral Coefficients ◽  
Sound Classification ◽  
Clustering Model ◽  
Peak Detection Algorithm

Traditionally, the heart sound classification process is performed by first finding the elementary heart sounds of the phonocardiogram (PCG) signal. After detecting sounds S1 and S2, the features like envelograms, Mel frequency cepstral coefficients (MFCC), kurtosis, etc., of these sounds are extracted. These features are used for the classification of normal and abnormal heart sounds, which leads to an increase in computational complexity. In this paper, we have proposed a fully automated algorithm to localize heart sounds using K-means clustering. The K-means clustering model can differentiate between the primitive heart sounds like S1, S2, S3, S4 and the rest of the insignificant sounds like murmurs without requiring the excessive pre-processing of data. The peaks detected from the noisy data are validated by implementing five classification models with 30 fold cross-validation. These models have been implemented on a publicly available PhysioNet/Cinc challenge 2016 database. Lastly, to classify between normal and abnormal heart sounds, the localized labelled peaks from all the datasets were fed as an input to the various classifiers such as support vector machine (SVM), K-nearest neighbours (KNN), logistic regression, stochastic gradient descent (SGD) and multi-layer perceptron (MLP). To validate the superiority of the proposed work, we have compared our reported metrics with the latest state-of-the-art works. Simulation results show that the highest classification accuracy of 94.75% is achieved by the SVM classifier among all other classifiers.

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