A Detection System of Infantâ€™s Cry, Using Fuzzy Classification: from Theory to Practice
Mohammad Kia, Shabnam Kia, Kamaledin Mousavi Mashhadi
Journal of Information Organization, Vol. 2, No. 3, 2012.
DOI: 
Link: http://www.dline.info/jio/fulltext/v2n3/2.pdf
Abstract:
Nowadays, it is inconvenient for housekeeper parents to constantly watch over their newborn baby while doing their work or chores. This paper proposes a simple voice recognition system which can be applied practically for designing a device with capability to detect a babyâ€™s cry and informing the parents automatically. There are a lot of similar projects and experiments which have been performed recently, but most of them are about recognizing and classifying different types of crying (like for hunger, etc.) and have used complex methods of implementation such as neural network. But in this paper our aim is to merely detect infantâ€™s crying, and our solution is to use a fuzzy classifier which is easy to implement and fast to execute. The overall algorithm is to evaluate the resemblance of the infantâ€™s voice signal with the data stored in a database, which is already prepared by recording some cry and laughter samples, using an automatic fuzzy classifier system which can lead to detection of cry or laughter. This algorithm can serve as a reliable foundation on which the future creation of a portable real-time, automatic voice detection device can be based. It is a pretty formidable task to implement complex algorithms, such as neural networks, on common available microcontrollers, however we proposed a much simpler algorithm which enables us to develop a real-time and low cost device. To evaluate the algorithm, we have created a database of sample cry and laughter signals and developed a sample Matlab program for carrying out the real-time frequency-domain calculations and a sample visual program in Labview programming environment for interfacing with user.