Abstract
Data compression is always advisable when it comes to handling and processing information quickly and efficiently. There are two main problems that need to be solved when it comes to handling data; store information in smaller spaces and processes it in the shortest possible time. When it comes to infant cry analysis (ICA), there is always the need to construct large sound repositories from crying babies. Samples that have to be analyzed and be used to train and test pattern recognition algorithms; making this a time consuming task when working with uncompressed feature vectors. In this work, we show a simple, but efficient, method that uses Fuzzy Relational Product (FRP) to compresses the information inside a feature vector, building with this a compressed matrix that will help us recognize two kinds of pathologies in infants; Asphyxia and Deafness. We describe the sound analysis, which consists on the extraction of Mel Frequency Cepstral Coefficients that generate vectors which will later be compressed by using FRP. There is also a description of the infant cry database used in this work, along with the training and testing of a Time Delay Neural Network with the compressed features, which shows a performance of 96.44% with our proposed feature vector compression.
For the present work we used the “Chillanto Database” which is property of the Institúto Nacional de Astrofísica Óptica y Electrónica (INAOE) - CONACYT, México. We like to thank Dr. Carlos A. Reyes-García, Dr. Edgar M. Garcia-Tamayo Dr. Emilio Arch-Tirado and his INR-Mexico group for their dedication of the collection of the Infant Cry data base.
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References
Orozco Garcia, J., Reyes Garcia, C.A.: Mel-Frequency Cepstrum coefficients Extraction from Infant Cry for Classification of Normal and Pathological Cry with Feedforward Neural Networks, ESANN, Bruges, Belgium (2003)
Petroni, M., Malowany, A.S., Celeste Johnston, C., Stevens, B.J.: Identification of pain from infant cry vocalizations using artificial neural networks (ANNs). In: The International Society for Optical Engineering, vol. 2492, Part two of two. Paper #: 2492-79 (1995)
Ekkel, T.: Neural Network-Based Classification of Cries from Infants Suffering from Hypoxia-Related CNS Damage, Master’s Thesis. University of Twente, The Netherlands (2002)
Cano, S.D., Daniel, I.: Escobedo y Eddy Coello, El Uso de los Mapas Auto-Organizados de Kohonen en la Clasificación de Unidades de Llanto Infantil. In: Voice Processing Group, 1st Workshop AIRENE, Universidad Católica del Norte, Chile, pp. 24–29 (1999)
Galaviz, O.F.R.: Infant Cry Classification to Identify Deafness and Asphyxia with a Hybrid System (Genetic Neural). Master’s Thesis on Computer Science, Apizaco Institute of Technology (ITA) (March 2005)
Galaviz, O.F.R., García, C.A.R.: Infant Cry Classification to Identify Hypoacoustics and Aphyxia Comparing an Evolutionary-Neural System with a Simple Neural Network System. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 949–958. Springer, Heidelberg (2005)
Reyes-Galaviz, O.F., Cano-Ortiz, S.D., Reyes-Garca, C.A.: Evolutionary-Neural System to Classify Infant Cry Units for Pathologies Identification in Recently Born Babies. IEEE Computer Society, Los Alamitos (2008)
Gold, B., Morgan, N.: Speech and Audio Signal Processing: Processing and perception of speech and music. John Wiley & Sons, Inc. Chichester (2000)
Sanchez, E.: Resolution of composite fuzzy relation equations. Inform. and Control (30), 38–49 (1976)
di Nola, A., Pedrycz, W., Sessa, S., Pei-Zhuang, W.: Fuzzy Relation Equation Under A Class Of Triangular Norms: A Survey and New Results. Stochastica 3(2) (1994)
Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 338–353 (1965)
Mc Neill, D., Freiberger, P.: Fuzzy Logic. Simon & Shuster, New York (1993)
Garcia, C.A.R.: Sistemas Difusos (Fuzzy Systems): Fundamentos y Aplicaciones. In: Tutorials and Workshops of the IV Mexican International Conference on Artificial Intelligence, ITESM Monterrey, Mxico, November 14-18 (2005) ISBN 968-891-094-5
Hirota, K., Pedrycz, W.: Fuzzy Relational Compression. IEEE Transactions on Systems, man, and cybernetics,Part B: Cybernetics 29(3), 1–9 (1999)
Hirota, K., Pedrycz, W.: Data Compression with fuzzy relational equations. Fuzzy Sets and Systems 126, 325–335 (2002)
Karayiannis, N.B., Pai, P.I.: Fuzzy vector quantization algorithms and their application in image compression. IEEE Transactions on Image Processing 4(9), 1193–1201 (1995)
Nobuhara, H., Pedrycz, W., Hirota, K.: Fast Solving Method of Fuzzy Relational Equation and Its Application to Lossy Image Compression and Reconstruction. IEEE Transactions on Fuzzy Systems 8(3), 325–334 (2000)
Neural Network Toolbox Guide, Matlab r2009a, Developed by MathWoks, Inc. (2009)
Boersma, P., Weenink, D.: Praat v. 4.0.8. A system for doing phonetics by computer. Institute of Phonetic Sciences of the University of Amsterdam (February 2002)
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Reyes-Galaviz, O.F., Reyes-García, C.A. (2009). Fuzzy Relational Compression Applied on Feature Vectors for Infant Cry Recognition. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_37
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DOI: https://doi.org/10.1007/978-3-642-05258-3_37
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