Abstract
We propose an approach of generating a hybrid feature set and using prior knowledge in a multi-stage CNNs for robust infant sound classification. The dominant and auxiliary features within the set are beneficial to enlarge the coverage as well as keeping a good resolution for modeling the diversity of variations within infant sound. The novel multi-stage CNNs method work together with prior knowledge constraints in decision making to overcome the limited data problem in infant sound classification. Prior knowledge either from rules or from statistical results provides a good guidance for searching and classification. The effectiveness of proposed method is evaluated on commonly used Dustan Baby Language Database and Baby Chillanto Database. It gives an encouraging reduction of 4.14% absolute classification error rate compared with the results from the best model using one-stage CNN. In addition, on Baby Chillanto Database, a significant absolute error reduction of 5.33% is achieved compared to one-stage CNN and it outperforms all other existing related studies.
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Acknowledgement
We’d like to thank Dr. Eduard Franti for sharing the Dunstan Baby database and thank Dr. Orion Reyes and Dr. Carlos A. Reyes for providing the access to the Baby Chillanto database. We want to express our deepest gratitude to Dr. Carlos A. Reyes-Garcia, Dr. Emilio Arch-Tirado and his INR-Mexico group, and Dr. Edgar M. Garcia-Tamayo for their dedication in the collection of the Infant Cry database. We also want to gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.
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Ji, C., Basodi, S., Xiao, X., Pan, Y. (2020). Infant Sound Classification on Multi-stage CNNs with Hybrid Features and Prior Knowledge. In: Xu, R., De, W., Zhong, W., Tian, L., Bai, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2020. AIMS 2020. Lecture Notes in Computer Science(), vol 12401. Springer, Cham. https://doi.org/10.1007/978-3-030-59605-7_1
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DOI: https://doi.org/10.1007/978-3-030-59605-7_1
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