We approach the challenging problem of generating highlights from sports broadcasts utilizing audio information only. A language-independent, multi-stage classification approach is employed for detection of key acoustic events which then act as a platform for summarization of highlight scenes. Objective results and human experience indicate that our system is highly efficient.
The Emoti-Chair is a novel technology to enhance entertainment through vibrotactile stimulation. We assessed the experience of this technology in two workshops. In the first workshop, deaf film-makers experimented with creating vibetracks for a movie clip using a professional movie editing software. In the second workshop, trained opera singers sang and 'felt' their voice through the Emoti-Chair. Participants in both workshops generally found the overall experience to be exciting and they were motivated to use the Chair for upcoming projects.
IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cite as: Panigrahi, K.B.; Baijal, A.; Krishna Chaitanya, P.; Nayak, P.P., "Power quality analysis using complex wavelet transform," IEEE Joint International
Conference on Power Electronics, Drives and Energy Systems (PEDES) & 2010Power India, pp.1-5, 2010 This is the 'accepted' version of the paper. The 'published' version can be found here: DOI: http://dx.doi.org/10.1109/PEDES.2010.5712564Abstract--This paper deals with analysis of power signals using complex wavelet transform. In the first step power signals containing sag, swell, harmonic, sag-harmonic, swell harmonic, transient and spike were generated using Matlab. Various features like energy, kurtosis, entropy, skewness etc. were extracted using 'db4' and complex wavelet decomposition up to 11 levels. Next, an extensive database of these features was created. A neural network based on these parameters was trained and tested. It has been shown that the accuracy achieved by using complex wavelet is higher than obtained by the use of 'db4' wavelet.
Experiencing images with suitable music can greatly enrich the overall user experience. The proposed image analysis method treats an artwork image differently from a photograph image. Automatic image classification is performed using deep-learning based models. An illustrative analysis showcasing the ability of our deep-models to inherently learn and utilize perceptually relevant features when classifying artworks is also presented. The Mean Opinion Score (MOS) obtained from subjective assessments of the respective image and recommended music pairs supports the effectiveness of our approach.
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