Browsing by Author "Chauhan, S.R."

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    Hard turning and machine tool: A review
    (Inderscience Publishers, 2015) Kumar, P.; Chauhan, S.R.; Aggarwal, A.
    Hard turning is a precise machining operation that imposes strict requirements on both the cutting and machine tools. During the last few decades, the application of hard turning has been increasing in various automotive industrial areas. With the development of new cutting tool, the precision and rigidity of machine tools have been improved to allow hard turning to become a viable process. The objective of this paper is to report a survey of the recent research progress in hard turning with cubic boron nitride tools in regard of machine tool, cutting tool, tool wear, tool geometry and surface issues. This paper report a survey of all the factors that affect the performance of hard turning process, in an attempt to achieve better understanding of hard turning process. A summary of the hard turning techniques is outlined and further a comparison of hard turning and grinding is discussed in brief. © 2015 Inderscience Enterprises Ltd.
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    Speech Summarization Using Prosodic Features and 1-D Convolutional Neural Network
    (Institute of Electrical and Electronics Engineers Inc., 2022) Chauhan, S.R.; Ambesange, S.; Koolagudi, S.G.
    In this work, we have presented a method for speech summarization of audiobooks without converting them into the transcript. The model used is the 1-D convolutional neural network. The audio is segmented into sentences based on the silence between two consecutive sentences. We have used acoustic features of the sentence audio as input to our model. The output of our model is binary, which tells us whether to include this sentence in our summary or not. Thus, we have converted the task of speech summarization into a classification task. Then we have concatenated the classified audio chunks into one summary. We have compared the generated summary against the manually done summary. For better insights, we have used a text summarizer as a reference to see what the summary should include. The transcript is used for only that; otherwise, our method is independent of the text. The results obtained show us a possibility of a language-independent audio summarizer that retains the audio quality since we have used the original audio in our summary. © 2022 IEEE.