Article Fingerprint
ReserarchID
0G413
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.
Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.
This paper attempted to assess the attitudes of students in
Advances in technology have created the potential for a new
Inclusion has become a priority on the global educational agenda,
The spectral centroid is one of the useful low level features of a signal that was proposed for speech-music classification, speech recognition and musical instrument classification, and was also considered one of the lowlevel features to describe the audio content in MPEG-7 Content Description and Interface Standard. When the spectral centroid is computed from practical data, the estimate is different from the true expected theoretical value. Moreover, the behavior of the estimation error, when computed from finite length data i.e. from a short segment of signal would of high interest because most of the classification algorithms use dynamic features as the signals are nonstationary. In this paper, windowing effects on the spectral centroid estimation are investigated considering some well structured signals that appear frequently in speech and audio content. A novel algorithm is proposed to counter the window effects and better estimation of spectral centroid.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
