Classification of Heart Health by LPC and MFCC Coefficients and Statistical Features

Soto-Murillo, Manuel A.; Bañuelos, Karen E. Villagrana; Rodriguez-Ruiz, Julieta G.; Salinas-González, Jared D.; Galván-Tejada, Carlos E.; Gamboa-Rosales, Hamurabi; Galván-Tejada, Jorge I.

doi:10.1007/978-3-030-30648-9_15

Cited by 1 publication

(2 citation statements)

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“…A filter bank was obtained for each window of the speech signal. The first filter obtained was very narrow, indicating the amount of energy present near 0 Hz [ 40 ]. As the frequencies increase, the filters expand and the variations are smaller.…”

Section: Methodsmentioning

confidence: 99%

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Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Park

Kim

Moon

et al. 2021

Animals

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The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

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Section: Methodsmentioning

confidence: 99%

“…

where P(k) is the power spectral density and k represents the number of windows that go from the first to the k-th window of the cattle’s call. Subsequently, the logarithm of the filter bank energy was calculated [ 40 ]. This work has the effect of closely matching the obtained traits to what humans actually hear.…”

Section: Methodsmentioning

confidence: 99%