Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Author: search.filters.author.Nagesh, S.

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    A robust speech rate estimation based on the activation profile from the selected acoustic unit dictionary
    (Institute of Electrical and Electronics Engineers Inc., 2016) Nagesh, S.; Yarra, C.; Deshmukh, O.D.; Ghosh, P.K.
    A typical solution for the speech rate estimation consists of two stages, which involves first computing a short-time feature contour such that most of peaks of the contour correspond to the syllable nuclei followed by the detection of the peaks of the contour corresponding to the syllable nuclei. Temporal correlation selected subband correlation (TCSSBC) is often used as a feature contour for the speech rate estimation in which correlation within and across a few selected sub-band energies are computed. In this work, instead of a fixed set of sub-bands, we learn them in a data-driven manner using a dictionary learning approach. Similarly, instead of the energy contours, we use the activation profile from the learned dictionary elements. We found that the peaks detected from the data-driven approach significantly improve the speech rate estimation when combined with the traditional TCSSBC approach using a proposed peak-merging strategy. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora. Except Switchboard, the correlation coefficient for the speech rate estimation using the proposed approach is found to be higher than those by the TCSSBC technique - 3.1% and 5.2% (relative) improvements for TIMIT and CTIMIT respectively. © 2016 IEEE.
  • No Thumbnail Available
    Item
    Simulation of Hemodynamics Phenomenon Using Computational Fluid Dynamics for Enhanced Diagnostics and Prognosis
    (Institute of Electrical and Electronics Engineers Inc., 2016) Hegde, S.S.; Deb, A.; Nagesh, S.
    Computational bio-mechanics is developing rapidly as a non-invasive tool to assist the medical fraternity to help both diagnosis and prognosis of human body related issues such as injuries, cardio-vascular dysfunction, atherosclerotic plaque etc. Any system that would help either assist diagnosis prognosis would be a boon to the doctors and medical society in general. Some work also has been done in the area related to the use of computational fluid mechanics to understand the flow of blood through the human body, an area of hemodynamics. Since cardio-vascular diseases are one of the main causes of loss of life, understanding of the blood flow with and without constraints (such as blockages), providing alternate methods of prognosis and further solutions to take care of issues related to blood flow would help save valuable life of such patients. This work attempts to use computational fluid dynamics (CFD) to solve specific problems related to hemodynamics. In particular mathematical modeling of the blood flow in arteries in the presence of successive blockages has been analyzed using CFD. Also considered is the effect of increase in Reynolds number on wall shear stress values. Also, the concept of fluid structure interaction has been used during analysis. © 2015 IEEE.
  • No Thumbnail Available
    Item
    A high resolution ENF based multi-stage classifier for location forensics of media recordings
    (Institute of Electrical and Electronics Engineers Inc., 2017) Suresha, P.B.; Nagesh, S.; Roshan, P.S.; Gaonkar, P.A.; Nisha Meenakshi, G.N.; Ghosh, P.K.
    Media recordings, when captured close to active power system components, are known to be influenced by the electromagnetic interference caused by those power grid components. This electromagnetic interference manifests itself in such media recordings in the form of time-varying frequency components around the electric network frequency (ENF) of the power grid. For example, the ENF of the Indian power grid has a nominal value 50Hz. Classification of a given media signal into the grid or region of recording using the electric network frequency (ENF) is vital in location forensics. In this work, we use power recordings and audio recordings captured from 12 different grids around the globe. To use the variations in the ENF from the media signals for region-of-recording classification, we propose a high resolution ENF extraction technique. We also propose the use of a multi-stage support vector machine (SVM) based classification system. We find that the proposed system outperforms the existing baseline scheme for region-of-recording classification, by yielding an improvement in the overall accuracy by 17.33%. © 2017 IEEE.