Conference Papers

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

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    Identifying gamakas in Carnatic music
    (Institute of Electrical and Electronics Engineers Inc., 2015) Vyas, H.M.; Suma, S.M.; Koolagudi, S.G.; Guruprasad, K.R.
    In this work, an effort has been made to identify the gamakas present in a given piece of Carnatic music clip. Gamakas are the beautification elements used to improve the melody. The identification of gamaka is very important stage in note transcription. In the proposed method, features that correspond to melodic variations such as pitch and energy are used for characterizing the gamakas. The input pitch contour is modelled using Hidden Markov Model with 3 states, namely Attack, Sustain and Decay. These states correspond to ups and downs in the melody of the music. The system is validated using a comprehensive data set consisting 160 songs from 8 different ragas. The average accuracy of 75.86% is achieved using this method. © 2015 IEEE.
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    Development of Voice Activated Ground Control Station
    (Elsevier B.V., 2016) Rahul, D.K.; Veena, S.; Lokesha, H.; Vinay, S.; Kumar, B.P.; Ananda, C.M.; Durdi, V.B.
    This paper chronicles the development of Automatic Speech Recognition (ASR) system that can be integrated to Ground Control Station (GCS) of MAVs to achieve voice activation. The first part of the paper highlights the nature of aerospace speech signals and hence the issues to be considered while designing a voice activated aerospace application. The second part describes the development and integration of an ASR capability to the GCS. © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
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    Hidden Markov Model for Hard Disk Drive Failure Detection
    (Institute of Electrical and Electronics Engineers Inc., 2024) Harish, A.; Prakash, G.; Nair, R.R.; Iyer, V.B.; Mohan, B.R.; Das, M.
    Understanding disk failures is crucial for both disk manufacturers and users, enabling the production of more dependable disk drives and the establishment of robust storage systems. Detecting disk failure has been found to be facilitated by the use of observable disk properties, especially those provided by the Self-Monitoring and Reporting Technology (SMART) system. In our paper, we leverage the capabilities of the SMART time series dataset to achieve an overall accuracy of 92% in disk failure detection. © 2024 IEEE.