TY - GEN
T1 - Campus Bullying Detection Algorithm Based on Audio
AU - Liu, Tong
AU - Ye, Liang
AU - Han, Tian
AU - Seppänen, Tapio
AU - Alasaarela, Esko
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - With the continuous breakthroughs in various technologies, voice recognition has become a research hotspot. It is a method to detect the phenomenon of bullying in time by detecting whether the campus bullying emotion is contained in the voice. This paper builds a convolutional neural network model to recognize speech emotions. Firstly, pre-process the audio data, then extract the MFCC feature parameters from the pre-processed audio data, and finally design a classification algorithm. This paper selects the CASIA database, which has a total of 300 voice audios, including six emotions: angry, scared, happy, neutral, sad, and surprised. Using fivefold cross-validation to test the performance of the model, the accuracy of the classification algorithm is 68.51%. Finally, the classification algorithm is used to perform emotion recognition on a test sample selected from a campus bullying movie section. This section shows “fear” emotion, and the algorithm judges that the audio shows “fear” emotion. The actual scenes are consistent, indicating that the classification algorithm in this paper has certain stability and practicability.
AB - With the continuous breakthroughs in various technologies, voice recognition has become a research hotspot. It is a method to detect the phenomenon of bullying in time by detecting whether the campus bullying emotion is contained in the voice. This paper builds a convolutional neural network model to recognize speech emotions. Firstly, pre-process the audio data, then extract the MFCC feature parameters from the pre-processed audio data, and finally design a classification algorithm. This paper selects the CASIA database, which has a total of 300 voice audios, including six emotions: angry, scared, happy, neutral, sad, and surprised. Using fivefold cross-validation to test the performance of the model, the accuracy of the classification algorithm is 68.51%. Finally, the classification algorithm is used to perform emotion recognition on a test sample selected from a campus bullying movie section. This section shows “fear” emotion, and the algorithm judges that the audio shows “fear” emotion. The actual scenes are consistent, indicating that the classification algorithm in this paper has certain stability and practicability.
KW - MFCC
KW - Neural networks
KW - Speech emotion recognition
UR - https://www.scopus.com/pages/publications/85111359313
U2 - 10.1007/978-981-15-8411-4_57
DO - 10.1007/978-981-15-8411-4_57
M3 - 会议稿件
AN - SCOPUS:85111359313
SN - 9789811584107
T3 - Lecture Notes in Electrical Engineering
SP - 420
EP - 424
BT - Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems
A2 - Liang, Qilian
A2 - Wang, Wei
A2 - Liu, Xin
A2 - Na, Zhenyu
A2 - Li, Xiaoxia
A2 - Zhang, Baoju
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020
Y2 - 4 July 2020 through 5 July 2020
ER -