Background:
This article represents a new method of classifying the heart sound status using the loudness features from the heart sound.
Materials and methods:
The method includes the following 3 main steps. First, the heart sound, which is usually found noisy, is heavily filtered by a 6th-order Chebyshev-I filter. The heart sound is then segmented using the event synchronous method to separate the sounds into the first heart sound, the systole and the second heart sound, the diastole. In the second step, the heart sound features namely maximum loudness index and minimum loudness index are obtained from the spectrogram of the sound by taking the row means. As a third step, the heart sound is classified using the Gaussian mixture model approach to categorize the sounds.
Results:
This method has been tested on a very large database of heart sounds consisting of over 3000 heart sounds recordings with a success rate of 97.77%.
Conclusion:
Only 2 features are used in this method namely, minimum loudness index and maximum loudness index. Classification of sounds using these 2 features yields high accuracy even under noisy conditions and is comparable to any state-of-the-art technique.
In this paper, we investigate the face recognition problem via clustering of frontal face images represented in frequency domain by low frequency Discrete Cosine Transform (DCT) coefficients. Our approach termed as Class Specific Space Model (CSSM) is based on the assumption that faces of different subjects are clustered in different low dimensional subspace of the feature space. The proposed approach uses 2D-DCT for feature extraction, each of the class clusters in the feature space are later modeled under Gaussian mixture model framework by a set of parameters which best fit the data. The proposed approach is tested on AR face database and its effectiveness in terms of identification rate is compared with the conventional IPCA and DLDA-SVM based classifiers.
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