#6204. Recognition of sick pig cough sounds based on convolutional neural network in field situations

August 2026publication date
Proposal available till 13-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Animal Science and Zoology;
Agronomy and Crop Science;
Forestry;
Aquatic Science;
Computer Science Applications;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №6204.1 Contract №6204.2 Contract №6204.3 Contract №6204.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
Coughing is an obvious respiratory disease symptom, which affects the airways and lungs of pigs. In pig houses, continuous online monitoring of cough sounds can be used to build an intelligent alarm system for disease early detection. Owing to complicated interferences in piggery, recognition of pig cough sound becomes difficult. Although a lot of algorithms have been proposed to recognize the pig cough sounds, the recognition accuracy in field situations still needs enhancement. The purpose of this research is to provide a highly accurate pig cough recognition method for the respiratory disease alarm system. We propose a classification algorithm based on the fine-tuned AlexNet model and feature of the spectrogram. With the advantages of the convolutional neural network in image recognition, the sound signals are converted into spectrogram images for recognition, to enhance the accuracy. We compare the proposed algorithms performance with the probabilistic neural network classifier and some existing algorithms.
Keywords:
Convolutional neural network; Cough recognition; Respiratory diseases detection; Sound classification

Contacts :
0