Name
Abstract | ||
|---|---|---|
Broilers produce abnormal sounds such as cough and snore when they suffer from respiratory diseases. The aim of this research work was to develop a method for broiler abnormal sound detection. The sounds were recorded in a broiler house for one week (24/7). There were 20 thousand white feather broilers reared on the floor in a building. Results showed that the developed recognition algorithm, using wavelet transform Mel frequency cepstrum coefficients (WMFCCs), correlation distance Fisher criterion (CDF), and hidden Markov model (HMM), provided an average accuracy, precision, recall, and F1 of 93.8%, 94.4%, 94.1%, and 94.2%, respectively, for broiler sound samples. The results indicate that sound analysis can be used in broiler respiratory assessment in a commercial broiler farm. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1155/2020/2985478 | JOURNAL OF SENSORS |
Field | DocType | Volume |
Computer vision,Mel-frequency cepstrum,Sound detection,Artificial intelligence,Recognition algorithm,Engineering,Broiler,Statistics,Hidden Markov model,Fisher criterion,Sound analysis,Wavelet transform | Journal | 2020 |
ISSN | Citations | PageRank |
1687-725X | 1 | 0.38 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longshen Liu | 1 | 1 | 0.38 |
| Bo Li | 2 | 8 | 8.65 |
| Ruqian Zhao | 3 | 1 | 0.38 |
| Wen Yao | 4 | 1 | 0.38 |
| Mingxia Shen | 5 | 1 | 1.06 |
| Ji Yang | 6 | 1 | 0.38 |