In speech processing gender clustering and classification is the most outstanding and challenging task. In both gender clustering and classification, one the most vital processes carried out is the selection of features. In speech processing, pitch is the most often used feature for gender clustering and classification. It is essential to note that compared to a female speech the pitch value of a male speech is much different. Also, in terms of frequency there is a considerable dissimilarity between the male and female speech. In some situations, either the frequency of male is almost same as female or the frequency of female is same as male. It is difficult to find out the exact gender in such conditions. This paper focus on rectifying these practical obstacles by extracting three significant features, namely, energy entropy, zero crossing rate, and short time energy. Gender clustering is performed based on these features. However, by means of Euclidean distance, Mahalanobis distance, Manhattan distance & Bhattacharyya distance methods the clustering performance is analyzed. Using fuzzy logic, neural network, hybrid neuro-fuzzy, and support vector machine the gender classification is done. A benchmark dataset and real-time dataset is used for testing to make sure the reliability of the performance. The test results show the performance of various techniques and distance algorithms for different datasets
One of the most important processes in speech processing is gender classification. Generally gender classification is done by considering pitch as feature. In general the pitch value of female is higher than the male. In some cases, pitch value of male is higher and female is low, in that cases this classification will not obtain the exact result. By considering this drawback here proposed a gender classification method which considers three features and uses fuzzy logic and neural network to identify the given speech signal belongs to which gender. For training fuzzy logic and neural network, training dataset is generated by considering the above three features. After completion of training, a speech signal is given as input, fuzzy and neural network gives an output, for that output mean value is taken and this value gives the speech signal belongs to which gender. The result shows the performance of our method in gender classification.
A framework for detecting and recording the flaws that happen during the usage of web applications is designed and a library functionality to perform this is discussed in this paper. The recorded information can be stored at different levels of detail, commonly called the logging levels. For some modules more than others, it may be required to store more detailed information about any error that arises during its usage according to its importance. A Web Application also needs to print the stack trace containing the error information on the web page when an error occurs for the user to understand the nature of the error. When dealing with legacy web applications, it is difficult to insert code. The proposed and designed framework is tested with a web application called KicKart.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations鈥揷itations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.