The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a study on how the performance of Phonetic engine(PE) varies with different set of spectral features selected for it. An exclusive study is carried out with a PE developed in the Manipuri language. Here, we built the PE using phonetic transcriptions and modeling of each phonetic unit by Hidden Markov Model (HMM). The symbols of International Phonetic Alphabet (IPA) (revised in...
Automated musical genre classification using machine learning techniques has gained popularity for research and development of powerful tools to organize music collections available on web. Mel cepstral co-efficients (MFCC's) have been successfully used in music genre classification but they do not reflect the correlation between the adjacent co-efficients of Mel filters of a frame neither the relation...
The goal of this paper is to determine the object a person visually perceives by analyzing BOLD fMRI data. We use an fMRI dataset and analyze the effects of univariate and multivariate feature selection techniques. By performing dimensionality reduction with Principal Component Analysis (PCA), training with a Support Vector Classifier without a kernel and appropriate smoothing, we obtained a 93.16%...
In recent years, SRC has received many attentions for classification and identification tasks. This paper attempts to introduce a sparse representation based classification of EEG signal features and identification of associated activities or tasks. It uses wavelet and ICA processing of EEG signal for feature selection and dictionary training. Multiple dictionaries are trained and used for EEG signal...
Object classification in both images and videos is an important task within the field of computer vision. The process of classifying objects into predefined and semantically meaningful categories using its features is called object classification. Many researchers are working in this area to improve the accuracy of classification and to reduce the dimension of features extracted which are used for...
Biometrics represents the identity of individuals. Physical characteristics like voice, face, fingerprint, etc. are used to recognize individuals. Biometrics are used as a promising method for authentication, but use of these raw biometric data results in some privacy concerns. In this paper, we propose a system model for privacy preserving biometric authentication system for speech, face and fingerprint...
In this paper we use a Deep Neural Network (DNN) trained on data collected from the visual media-sharing social platform Instagram account of a popular Indian lifestyle magazine to predict the popularity of future posts. This predicted popularity of the post can be used to decide advertising rates and measure performance metrics important for publishing strategy decisions. The DNN primarily uses growth...
Communication is an essential part of human life which paralytic patients with locked-in syndrome are deprived of. In locked-in syndrome, the patient cannot move any of his voluntary muscles except the eyes. Taking this into consideration, the proposed system is designed to detect the face and pupil of the patient through a standard webcam using Haar cascade classifiers and Circular Hough Transform...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.