Vinayak Abrol

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In practical Cognitive Radio Networks for Mobile Communication cluster based topology is used to increase spectrum detection performance. The energy of Head Node depletes faster than the member nodes and thus rotation of cluster head is needed for the energy efficiency of Cognitive Radio Networks. The level of hierarchy is increased in this approach by(More)
Reconstruction of a signal based on Compressed Sensing (CS) framework relies on the knowledge of the sparse basis & measurement matrix used for sensing. While most of the studies so far focus on the application of CS in fields of images, radar, astronomy etc.; we present our work on application of CS in field of speech/Audio processing. This work shows(More)
In this paper a sparse representation based feature is proposed for the tasks in speech recognition. Dictionary plays an important role in order to get a good sparse representation. Therefore instead of using a single over complete dictionary, multiple signal adaptive dictionaries are used. A novel principal component analysis (PCA) based method is proposed(More)
Cognitive Radio Networks provide a solution to the spectrum scarcity in wireless communication. It works on principle of dynamic spectrum management, which enables the secondary user to use the licensed spectrum of primary user opportunistically. To implement it we have to detect the absence of primary signal technically called detecting a spectrum hole. In(More)
This paper proposes an approach based on compressed sensing to reduce the footprint of speech corpus in unit selection based speech synthesis (USS) systems. It exploits the observation that speech signal can have a sparse representation (in suitable choice of basis functions) and can be estimated effectively using the sparse coding framework. Thus, only few(More)
Extracting inherent patterns from large data using decompositions of data matrix by a sampled subset of exemplars has found many applications in machine learning. We propose a computationally efficient algorithm for adaptive exemplar sampling, called fast exemplar selection (FES). The proposed algorithm can be seen as an efficient variant of the oASIS(More)