| Citation: | Xiang XIE, Liqiang ZHANG, Jing WANG. Application of Residual Network to Infant Crying Recognition[J]. Journal of Electronics & Information Technology, 2019, 41(1): 233-239. doi: 10.11999/JEIT180276
|
The deep learning model based on the residual network and the spectrogram is used to recognize infant crying. The corpus has balanced proportion of infant crying and non-crying samples. Finally, through the 5-fold cross validation, compared with three models of Support Vector Machine (SVM), Convolutional Neural Network (CNN) and the cochleagram residual network based on Gammatone filters (GT-Resnet), the spectrogram based residual network gets the best F1-score of 0.9965 and satisfies requirements of real time. It is proved that the spectrogram can react acoustics features intuitively and comprehensively in the recognition of infant crying. The residual network based on spectrogram is a good solution to infant crying recognition problem.
|
于洪志, 刘思思. 三个月婴儿啼哭声的声学分析[C]. 全国人机语音通讯学术会议, 西安, 2011: 1–4.
YU Hongzhi and LIU Sisi. Crying sound learning analysis of three months baby[C]. National Conference on Man-Machine Speech Communication, Xi’an, China, 2011: 1–4.
|
|
王之禹, 雷云珊. 婴儿啼哭声的声学特征[C]. 中国声学学会2006年全国声学学术会议, 厦门, 2006: 389–390.
WANG Zhiyu and LEI Yunshan. Acoustic characteristic of infant cries[C]. National Conference on Acoustics. Acoustical Society of China, Xiamen, China, 2006: 389–390.
|
|
ABDULAZIZ Y and AHMAD S M S. Infant cry recognition system: A comparison of system performance based on mel frequency and linear prediction cepstral coefficients[C]. International Conference on Information Retrieval & Knowledge Management, Shah Alam, Malaysia, 2010: 260–263. doi: 10.1109/INFRKM.2010.5466907.
|
|
COHEN R and LAVNER Y. Infant cry analysis and detection[C]. Electrical & Electronics Engineers in Israel, Eilat, Israel, 2012: 1–5.
|
|
LAVNER Y, COHEN R, RUINSKIY D, et al. Baby cry detection in domestic environment using deep learning[C]. 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), Eilat, Israel, 2016: 1–5. doi: 10.1109/EEEI.2012.6376996.
|
|
TORRES R, BATTAGLINO D, and LEPAULOUX L. Baby cry sound detection: A comparison of hand crafted features and deep learning approach[C]. International Conference on Engineering Applications of Neural Networks. Springer, Cham, 2017: 168–179. doi: 10.1007/978-3-319-65172-9_15.
|
|
CHANG Chuanyu and LI Jiajing. Application of deep learning for recognizing infant cries[C]. IEEE International Conference on Consumer Electronics, Nantou, China, 2016: 1–2. doi: 10.1109/ICCE-TW.2016.7520947.
|
|
SHARAN R V and MOIR T J. Cochleagram image feature for improved robustness in sound recognition[C]. IEEE International Conference on Digital Signal Processing, Singapore, 2015: 441–444.
|
|
PATTERSON R D, NIMMO-SMITH I, HOLDSWORTH J, et al. An efficient auditory filterbank based on the gammatone function[C]. Proceedings of the 1987 Speech-Group Meeting of the Institute of Acoustics on Auditory Modelling, RSRE, Malvern, 1987: 2–18.
|
|
刘文举, 聂帅, 梁山, 等. 基于深度学习语音分离技术的研究现状与进展[J]. 自动化学报, 2016, 42(6): 819–833. doi: 10.16383/j.aas.2016.c150734
LIU Wenju, NIE Shuai, LIANG Shan, et al. Deep learning based speech separation technology and its developments[J]. Acta Automatica Sinica, 2016, 42(6): 819–833. doi: 10.16383/j.aas.2016.c150734
|
|
MITTAL V K. Discriminating features of infant cry acoustic signal for automated detection of cause of crying[C]. International Symposium on Chinese Spoken Language Processing, Tianjin, China, 2017: 1–5. doi: 10.1109/ISCSLP.2016.7918391.
|
|
RPSITA Y D and JUNAEDI H. Infant’s cry sound classification using Mel-Frequency Cepstrum Coefficients feature extraction and Backpropagation Neural Network[C]. International Conference on Science and Technology-Computer, Yogyakarta, Indonesia, 2017: 160–166. doi: 10.1109/ICSTC.2016.7877367.
|
|
雷云珊. 婴儿啼哭声分析与模式分类[D]. [硕士论文], 山东科技大学, 2006.
LEI Yunshan. Analysis and pattern classification of infants’ cry[D]. [Master dissertation], Shandong University of Science and Technology, 2006.
|
|
KRIZHEVAKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems, Nevada, USA, 2012: 1097–1105.
|
|
HE Kaiming, ZHANG Xianyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. Computer Vision and Pattern Recognition, Nevada, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
|
|
GVERES. donateacry-corpus[OL]. https://github.com/gveres/donateacry-corpus, 2017.3.
|
|
彭天强, 栗芳. 基于深度卷积神经网络和二进制哈希学习的图像检索方法[J]. 电子与信息学报, 2016, 38(8): 2068–2075. doi: 10.11999/JEIT151346
PENG Tianqiang and LI Fang. Image retrieval based on deep convolutional neural networks and binary hashing learning[J]. Journal of Electronics &Information Technology, 2016, 38(8): 2068–2075. doi: 10.11999/JEIT151346
|
|
CHANG Chihchung and LIN Chihjen. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1–27. doi: 10.1145/1961189.1961199
|
|
徐利强, 谢湘, 黄石磊, 等. 连续语音中的笑声检测研究与实现[C]. 全国声学学术会议, 武汉, 2016: 581–584.
XU Liqiang, XIE Xiang, HUANG Shilei, et al. Research and implementation of laughter detection in continuous speech[C]. National Conference on Acoustics. Acoustical Society of China, Wuhan, China, 2016: 581–584.
|
Figures(6) / Tables(5)
DownLoad:
