Heart Sound Analysis Using MFCC and Time Frequency Distribution

Kamarulafizam, I.; Salleh, Shaharuddin; Najeb, J. M.; Ariff, A. K.; Chowdhury, Amor

doi:10.1007/978-3-540-68017-8_102

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…Considering this issue, the appropriate method can be selected according to the area of use. The MFCC method takes an important place in the field of audio signal processing, thanks to producing effective features by reducing the margin of error of sound signals exposed to noise (Kamarulafizam et al 2007 ). Due to the logarithmic structure of the frequency bands in the MFCC method, it is close to the human system response, which makes this method different from other methods (Janse et al 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Manshouri

2021

Cogn Neurodyn

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Sound signals from the respiratory system are largely taken as tokens of human health. Early diagnosis of respiratory tract diseases is of great importance because, if delayed, it exerts irreversible effects on human health. The Coronavirus pandemic, which is deeply shaking the world, has revealed the importance of this diagnosis even more. During the pandemic, it has become the focus of researchers to differentiate symptoms from similar diseases such as influenza. Among these symptoms, the difference in cough sound played a distinctive role in research. Clinical data collected under the supervision of doctors in a reliable environment were used as the dataset consisting of 16 subjects suspected of COVID-19 with a specific patient demographic. Using the polymerase chain reaction test, the suspected subjects were divided into two groups as negative and positive. The negative and positive labels represent the patients with non-COVID and with a COVID-19 cough, respectively. Using the 3D plot or waterfall representation of the signal frequency spectrum, the salient features of the cough data are revealed. In this way, COVID-19 can be differentiated from other coughs by applying effective feature extraction and classification techniques. Power spectral density based on short-time Fourier transform and mel-frequency cepstral coefficients (MFCC) were chosen as the efficient feature extraction method. From among the classification techniques, the support vector machine (SVM) algorithm was applied to the processed signals in order to identify and classify COVID-19 cough. In terms of results evaluation, the cough of subjects with COVID-19 was detected with 95.86% classification accuracy thanks to the radial basis function (RBF) kernel function of SVM and the MFCC method. The diagnosis of COVID-19 coughs was performed with 98.6% and 91.7% sensitivity and specificity, respectively.

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Section: Discussionmentioning

confidence: 99%

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Manshouri

2021

Cogn Neurodyn

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“…As an effective acoustic feature, MFCCs, which represent the result of a cosine transform of the real logarithm of the short-term energy spectrum on a Melfrequency scale, 71 have been applied widely in the audio recognition due to its good performance. [72][73][74] The relationship between the frequency in the Mel scale and Hertz scale is shown as follow…”

Section: Mfccsmentioning

confidence: 99%

Detection of sand deposition in pipeline using percussion, voice recognition, and support vector machine

Cheng

Wang

Huo

et al. 2020

Structural Health Monitoring

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Deposits prevention and removal in pipeline has great importance to ensure pipeline operation. Selecting a suitable removal time based on the composition and mass of the deposits not only reduces cost but also improves efficiency. In this article, we develop a new non-destructive approach using the percussion method and voice recognition with support vector machine to detect the sandy deposits in the steel pipeline. Particularly, as the mass of sandy deposits in the pipeline changes, the impact-induced sound signals will be different. A commonly used voice recognition feature, Mel-Frequency Cepstrum Coefficients, which represent the result of a cosine transform of the real logarithm of the short-term energy spectrum on a Mel-frequency scale, is adopted in this research and Mel-Frequency Cepstrum Coefficients are extracted from the obtained sound signals. A support vector machine model was employed to identify the sandy deposits with different mass values by classifying energy summation and Mel-Frequency Cepstrum Coefficients. In addition, the classification accuracies of energy summation and Mel-Frequency Cepstrum Coefficients are compared. The experimental results demonstrated that Mel-Frequency Cepstrum Coefficients perform better in pipeline deposits detection and have great potential in acoustic recognition for structural health monitoring. In addition, the proposed Mel-Frequency Cepstrum Coefficients–based pipeline deposits monitoring model can estimate the deposits in the pipeline with high accuracy. Moreover, compared with current non-destructive deposits detection approaches, the percussion method is easy to implement. With the rapid development of artificial intelligence and acoustic recognition, the proposed method can realize higher accuracy and higher speed in the detection of pipeline deposits, and has great application potential in the future. In addition, the proposed percussion method can enable robotic-based inspection for large-scale implementation.

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“…Signal‐processing techniques are successful to some extent classifying the vehicles into healthy and faulty [2], bike and scooter [3], engine fault diagnosis [4–6] and gearbox fault diagnosis [7–9]. The reported works on vehicle detection, classification and fault diagnosis are mainly based on processing of acoustic signals fused with video, images, and infrared signals.…”

Section: Review Of Literaturementioning

confidence: 99%

“…Auscultation methods provide the information about a vast variety of internal body sounds originated from the heart, lungs, bowel and vascular disorders. A technique for heart sound analysis is illustrated, which employs time‐frequency distribution (TFD) analysis and Mel frequency cepstrum coefficients (MFCC) [9]. A multi‐resolution wavelet transform is demonstrated for electrocardiogram (ECG) feature extraction system [10].…”

Section: Introductionmentioning

confidence: 99%

Acoustic signal‐based approach for fault detection in motorcycles using chaincode of the pseudospectrum and dynamic time warping classifier

Anami¹,

Pagi²

2014

IET Intelligent Transport Systems

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Heart Sound Analysis Using MFCC and Time Frequency Distribution

Cited by 9 publications

References 11 publications

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Detection of sand deposition in pipeline using percussion, voice recognition, and support vector machine

Acoustic signal‐based approach for fault detection in motorcycles using chaincode of the pseudospectrum and dynamic time warping classifier

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