Indian Institute of Technology Madras

About: Indian Institute of Technology Madras is a facility organization based out in Chennai, Tamil Nadu, India. It is known for research contribution in the topics: Catalysis & Heat transfer. The organization has 20118 authors who have published 36499 publications receiving 590447 citations.

Characterization of Glottal Activity From Speech Signals

Abstract: The objective of this work is to characterize certain important features of excitation of speech, namely, detecting the regions of glottal activity and estimating the strength of excitation in each glottal cycle. The proposed method is based on the assumption that the excitation to the vocal-tract system can be approximated by a sequence of impulses of varying strengths. The effect due to an impulse in the time-domain is spread uniformly across the frequency-domain including at zero-frequency. We propose the use of a zero-frequency resonator to extract the characteristics of excitation source from speech signals by filtering out most of the time-varying vocal-tract information. The regions of glottal activity and the strengths of excitation estimated from the speech signal are in close agreement with those observed from the simultaneously recorded electro-glotto-graph signals. The performance of the proposed glottal activity detection is evaluated under different noisy environments at varying levels of degradation.

Correlational neural networks

Abstract: Common representation learning CRL, wherein different descriptions or views of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis CCA-based approaches and autoencoder AE-based approaches. CCA-based approaches learn a joint representation by maximizing correlation of the views when projected to the common subspace. AE-based methods learn a common representation by minimizing the error of reconstructing the two views. Each of these approaches has its own advantages and disadvantages. For example, while CCA-based approaches outperform AE-based approaches for the task of transfer learning, they are not as scalable as the latter. In this work, we propose an AE-based approach, correlational neural network CorrNet, that explicitly maximizes correlation among the views when projected to the common subspace. Through a series of experiments, we demonstrate that the proposed CorrNet is better than AE and CCA with respect to its ability to learn correlated common representations. We employ CorrNet for several cross-language tasks and show that the representations learned using it perform better than the ones learned using other state-of-the-art approaches.

Electrospun NiO nanofibers as cathode materials for high performance asymmetric supercapacitors

Abstract: Asymmetric supercapacitors (ASCs) based on aqueous electrolytes have received widespread attention in energy research in recent years because they provide high energy and power densities in addition to being ‘green electrolyte’. Herein, we report an ASC built with electrospun nanofibers of NiO as battery type cathode material and commercially available high surface area activated carbon as capacitor type anode material with appropriate mass loadings. We synthesized high aspect ratio nanofibers of NiO by simple and cost effective sol–gel based electrospinning followed by annealing. In the end, these nanofibers were composed of densely packed hexagonal nanoparticles of polycrystalline NiO having diameters of ∼15 nm. The ASC was capable of operating in the potential window of 1.5 V in 6 M KOH solution with a gravimetric capacitance of 141 F g−1 and energy density of 43.75 W h kg−1. The ASC showed high retention of the specific capacitance for 5000 galvanostatic charge–discharge cycles with improved coulombic efficiency.