Hybrid Neural Network Design and Implementation on FPGA for Infant Cry Recognition
It has been found that the infant’s crying has much information on its sound wave. For small infants crying is a form of communication, a very limited one, but similar to the way adults communicate. In this work we present the design of an Automatic Infant Cry Recognizer hybrid system, that classifies different kinds of cries, with the objective of identifying some pathologies in recently born babies. The system is based on the implementation of a Fuzzy Relational Neural Network (FRNN) model on a standard reconfigurable hardware like Field Programmable Gate Arrays (FPGAs). To perform the experiments, a set of crying samples is divided in two parts; the first one is used for training and the other one for testing. The input features are represented by fuzzy membership functions and the links between nodes, instead of regular weights, are represented by fuzzy relations. The training adjusts the relational weight matrix, and once its values have been adapted, the matrix is fixed into the FPGA. The goal of this research is to prove the performance of the FRNN in a development board; in this case we used the RC100 from Celoxica. The implementation process, as well as some results is shown.
KeywordsMembership Function Input Vector Field Programmable Gate Array Automatic Speech Recognition Fuzzy Neural Network
Unable to display preview. Download preview PDF.
- 2.Reyes, C.A.: On the design of a fuzzy relational neural network for automatic speech recognition. Doctoral Dissertation, The Florida State University, Tallahassee, Fl (1994)Google Scholar
- 3.Suaste-Rivas, I., Reyes-Galaviz, O.F., Diaz-Mendez, A., Reyes-Garcia, C.A.: A fuzzy relational neural network for pattern classification. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds.) CIARP 2004. LNCS, vol. 3287, pp. 358–365. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 4.Suaste-Rivas, I., Reyes-Galviz, O.F., Diaz-Mendez, A., Reyes-Garcia, C.A.: Implementation of a Linguistic Fuzzy Relational Neural Network for Detecting Pathologies by Infant Cry Recognition. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS, vol. 3315, pp. 953–962. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 5.Wasz-Höckert, O., Lind, J., Vuorenkoski, V., Partenen, T., Valanne, E.: The infant cry: a spectrographic and auditory analisis. Clin. Dev. Med. 29, 1–42 (1968)Google Scholar
- 6.Orozco, J., Reyes, C.A.: Mel-frequency cepstrum coefficients extraction from infant cry for classification of normal and pathological cry whit feed-forward neural networks. In: Proceedings of ESANN, Bruges, Belgium (2003)Google Scholar
- 7.RC100 User Manual: Celoxica. Version 1.2. United Kingdom (2001), http://www.celoxica.com/support/view_article.asp?ArticleID=376
- 8.Handel-C. Language Refence Manual: Celoxica Ltd. Version 1.2. United Kingdom (2001), http://www.celoxica.com/methodology/handelc.asp