Analysis of LFCC Feature Extraction in Baby Crying Classification using KNN Cry is a form of communication for children to express their feeling. Babyu0027s cry can be characterized according to its natural periodic tone and the change of voice. It has a base frequency (pitch) in range 250Hz to 600Hz. Through their babyu0027s cries detection, parents can monitor their baby remotely only in important condition. This study of sound recognition has two main processes, the first process is feature extraction and the second process is classification or determining the sound pattern. In the Linear Frequency Cepstral Coefficient (LFCC) method, the analysis of changes in preemphasis, numbers of filter bank and numbers of cepstral are conducted. The selection of the filter bank value which applied must be greater than the cepstral value which applied. Cepstral values is adjusted to get the better accuracy. The highest percentage of accuracy is 90% when this system uses 8 as the cepstral value and 3 as the nearest neighbor value, and all rules are considered the best value based on the test results. The use of LFCC as feature extraction method and KNearest Neighbor (K NN) classification can be implemented to detect the baby is crying or not so that it can be applied as a solution for parents to monitor their children remotely only in certain condition.

A Novel Method for Broiler Abnormal Sound Detection Using WMFCC and HMM Broilers produce abnormal sounds such as cough and snore when they suffer from respiratory diseases. The aim of this research work was to develop a method for broiler abnormal sound detection. The sounds were recorded in a broiler house for one week (247). There were 20 thousand white feather broilers reared on the floor in a building. Results showed that the developed recognition algorithm, using wavelet transform Mel frequency cepstrum coefficients (WMFCCs), correlation distance Fisher criterion (CDF), and hidden Markov model (HMM), provided an average accuracy, precision, recall, and F1 of 93.8%, 94.4%, 94.1%, and 94.2%, respectively, for broiler sound samples. The results indicate that sound analysis can be used in broiler respiratory assessment in a commercial broiler farm.