Enhancement and Comparative Analysis of Environmental Sound Classification Using MFCC and Empirical Mode Decomposition

2020 ◽  
pp. 227-235
Author(s):  
Ridhima Bansal ◽  
Namita Shukla ◽  
Maghav Goyal ◽  
Dhirendra Kumar
Keyword(s):  
Comparative Analysis ◽  
Empirical Mode Decomposition ◽  
Environmental Sound ◽  
Sound Classification ◽  
Mode Decomposition

Environmental sound classification using optimum allocation sampling based empirical mode decomposition

2020 ◽  
Vol 537 ◽  
pp. 122613 ◽  
Author(s):  
Saad Ahmad ◽  
Shubham Agrawal ◽  
Samta Joshi ◽  
Sachin Taran ◽  
Varun Bajaj ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Optimum Allocation ◽  
Environmental Sound ◽  
Sound Classification ◽  
Mode Decomposition

scholarly journals Empirical Mode Decomposition and Grey Level Difference for Lung Sound Classification

2021 ◽  
Vol 38 (1) ◽  
pp. 175-179
Author(s):  
Sugondo Hadiyoso ◽  
Achmad Rizal
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Extraction Method ◽  
Respiratory Health ◽  
Complex Nature ◽  
Lung Sound ◽  
Feature Extraction Method ◽  
Sound Classification ◽  
Level Difference ◽  
Mode Decomposition

Lung sound is one of the parameters of respiratory health. This sound has a specific character if there is a disease in the lungs. In some cases, it is difficult to distinguish one type of lung sound to another. It takes the expertise, experience and sensitivity of clinicians to avoid misdiagnosis. Therefore, many studies have proposed a feature extraction method combined with automatic classification method for the detection of lung disease through lung sound analysis. Since the complex nature of biological signals which are produced by complex processes, the multiscale method is an interesting feature extraction method to be developed. This study proposes an empirical mode decomposition (EMD) and a modified gray level difference (GLD) for a lung sound classification. The EMD was used to decompose the signal, and then GLD was measured on each decomposed signal as a feature set. There are five classes of lung sounds which were simulated in this study, including normal, wheeze, crackle, pleural rub, and stridor. Performance evaluation was carried out using a multilayer perceptron (MLP) and 3-fold cross-validation. This proposed method yielded the highest accuracy of 96.97%. This study outperformed several previous studies which were simulated on the same dataset. It is hoped that in the future, the proposed methods can be tested on larger datasets to determine the robustness of the methods.

scholarly journals Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor

2017 ◽  
Vol 14 (1) ◽  
pp. 166-173 ◽  
Author(s):  
Achmad Rizal ◽  
Risanuri Hidayat ◽  
Hanung Adi Nugroho
Keyword(s):  
Empirical Mode Decomposition ◽  
Lung Sound ◽  
Sound Classification ◽  
Mode Decomposition

scholarly journals Gearbox damage identification using Ensemble Empirical Decomposition method

2019 ◽  
Vol 252 ◽  
pp. 06005
Author(s):  
Kamil Jonak ◽  
Arkadiusz Syta
Keyword(s):  
Comparative Analysis ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Damage Identification ◽  
Ensemble Empirical Mode Decomposition ◽  
Physical Damage ◽  
Vibration Signals ◽  
Characteristic Frequencies ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

In this article, we have conducted a comparative analysis of vibration signals from helicopter aircraft propulsion transmissions, registered on an industrial research stand. We compared acceleration vibrations in the case of gears without physical damage and gears with one tooth missing. Based on recorded signals, we determined the values of indicators based on the statistical properties of signals and compared them with each other. For a more exact comparison, the distribution of the tested signals to the empirical modes using the EEMD (Ensemble Empirical Mode Decomposition) method was performed. This allows to treat individual modes as components of a signal at specific frequencies, and also prevents mixing of modes in individual components, which may take place in the classic EMD analysis. It should be noted that individual modes may correspond to characteristic frequencies for the operation of the transmission. When comparing the values of the most frequently used indicators, modes/frequencies in which the damage was most visible were indicated.

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