Combined spectral and speech features for pig speech recognition.
The sound of the pig is one of its important signs, which can reflect various states such as hunger, pain or emotional state, and directly indicates the growth and health status of the pig. Existing speech recognition methods usually start with spectral features. The use of spectrograms to achieve c...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0276778 |
| _version_ | 1797956119738449920 |
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| author | Xuan Wu Silong Zhou Mingwei Chen Yihang Zhao Yifei Wang Xianmeng Zhao Danyang Li Haibo Pu |
| author_facet | Xuan Wu Silong Zhou Mingwei Chen Yihang Zhao Yifei Wang Xianmeng Zhao Danyang Li Haibo Pu |
| author_sort | Xuan Wu |
| collection | DOAJ |
| description | The sound of the pig is one of its important signs, which can reflect various states such as hunger, pain or emotional state, and directly indicates the growth and health status of the pig. Existing speech recognition methods usually start with spectral features. The use of spectrograms to achieve classification of different speech sounds, while working well, may not be the best approach for solving such tasks with single-dimensional feature input. Based on the above assumptions, in order to more accurately grasp the situation of pigs and take timely measures to ensure the health status of pigs, this paper proposes a pig sound classification method based on the dual role of signal spectrum and speech. Spectrograms can visualize information about the characteristics of the sound under different time periods. The audio data are introduced, and the spectrogram features of the model input as well as the audio time-domain features are complemented with each other and passed into a pre-designed parallel network structure. The network model with the best results and the classifier were selected for combination. An accuracy of 93.39% was achieved on the pig speech classification task, while the AUC also reached 0.99163, demonstrating the superiority of the method. This study contributes to the direction of computer vision and acoustics by recognizing the sound of pigs. In addition, a total of 4,000 pig sound datasets in four categories are established in this paper to provide a research basis for later research scholars. |
| first_indexed | 2024-04-10T23:43:50Z |
| format | Article |
| id | doaj.art-9d2e8cc2cd96435ab76325a8032133b8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T23:43:50Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-9d2e8cc2cd96435ab76325a8032133b82023-01-11T05:32:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011712e027677810.1371/journal.pone.0276778Combined spectral and speech features for pig speech recognition.Xuan WuSilong ZhouMingwei ChenYihang ZhaoYifei WangXianmeng ZhaoDanyang LiHaibo PuThe sound of the pig is one of its important signs, which can reflect various states such as hunger, pain or emotional state, and directly indicates the growth and health status of the pig. Existing speech recognition methods usually start with spectral features. The use of spectrograms to achieve classification of different speech sounds, while working well, may not be the best approach for solving such tasks with single-dimensional feature input. Based on the above assumptions, in order to more accurately grasp the situation of pigs and take timely measures to ensure the health status of pigs, this paper proposes a pig sound classification method based on the dual role of signal spectrum and speech. Spectrograms can visualize information about the characteristics of the sound under different time periods. The audio data are introduced, and the spectrogram features of the model input as well as the audio time-domain features are complemented with each other and passed into a pre-designed parallel network structure. The network model with the best results and the classifier were selected for combination. An accuracy of 93.39% was achieved on the pig speech classification task, while the AUC also reached 0.99163, demonstrating the superiority of the method. This study contributes to the direction of computer vision and acoustics by recognizing the sound of pigs. In addition, a total of 4,000 pig sound datasets in four categories are established in this paper to provide a research basis for later research scholars.https://doi.org/10.1371/journal.pone.0276778 |
| spellingShingle | Xuan Wu Silong Zhou Mingwei Chen Yihang Zhao Yifei Wang Xianmeng Zhao Danyang Li Haibo Pu Combined spectral and speech features for pig speech recognition. PLoS ONE |
| title | Combined spectral and speech features for pig speech recognition. |
| title_full | Combined spectral and speech features for pig speech recognition. |
| title_fullStr | Combined spectral and speech features for pig speech recognition. |
| title_full_unstemmed | Combined spectral and speech features for pig speech recognition. |
| title_short | Combined spectral and speech features for pig speech recognition. |
| title_sort | combined spectral and speech features for pig speech recognition |
| url | https://doi.org/10.1371/journal.pone.0276778 |
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