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Silk (strings) and bamboo (winds) were two of the materials of the bayin ("eight sounds") classification system established during the Xi (Western) Zhou dynasty (1046 771 bc); the others were metal, stone, earth, skin, wood, and gourd.
In this paper, we compare three machine learning approaches for lung sounds classification.
In Section 4, TF analysis-based music classification and environmental sounds classification will be covered.
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In the literature, lung sound classification was made for a maximum of six classes.
These tasks include frequency-dependent amplification, feedback cancellation, background noise reduction, and environmental sound classification.
Experiments on novel algorithms for people fall detection and sound classification and localization are discussed.
An interesting study was performed on the focused but difficult problem of pandeiro sound classification [83, 89].
Likewise, Kay and Agarwal [73] used MFCC and continuous wavelet transform (DWT being the discrete counterpart) for heart sound classification.
Instead, there are several prominent approaches to environmental sound classification that have been advanced over the last several decades [5 7].
We prepared four data sets for each CNN and SVM algorithm to classify respiratory audio: (1) healthy versus pathological classification; (2) rale, rhonchus, and normal sound classification; (3) singular respiratory sound type classification; and (4) audio type classification with all sound types.
CNN and SVM algorithms were run comparatively to classify respiratory audio: (1) healthy versus pathological classification, (2) rale, rhonchus, and normal sound classification, (3) singular respiratory sound type classification, and (4) audio type classification with all sound types.
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