Conference Papers

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

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Author: search.filters.author.Ramteke, P.B.Subject: search.filters.subject.Random forest

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    Characterization of aspirated and unaspirated sounds in speech
    (Institute of Electrical and Electronics Engineers Inc., 2017) Ramteke, P.B.; Sadanand, A.; Koolagudi, S.G.; Pai, V.
    In this work, consonant aspiration and unaspiration phenomena are studied. It is known that, pronunciation of aspiration and unaspiration is characterized by the 'puff of air' released at the place of constriction in the vocal tract which is known as burst. Here, the properties of vowel immediately after the burst are studied for characterization of the burst. Excitation source signal estimated from the speech linear prediction residual is used for the task. The signal characteristics such as glottal pulse, duration of open, closed & return phases, slope of open & return phases, duration of burst, ratio of highest and lowest energies of signal and voice onset time (VOT) are explored to characterize aspiration and unaspiration. TIMIT English speech corpus is used to test the proposed approach. Random forest (RF) and support vector machine (SVMs) are used as classifiers to test the effectiveness of the features used for the task. An accuracy of 99.93% and 94.03% is achieved respectively. From the results, it is observed that the proposed features are robust in classifying the aspirated and unaspirated consonants. © 2017 IEEE.
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    Characterization of Consonant Sounds Using Features Related to Place of Articulation
    (Springer, 2020) Ramteke, P.B.; Hegde, S.; Koolagudi, S.G.
    Speech sounds are classified into 5 classes, grouped based on place and manner of articulation: velar, palatal, retroflex, dental and labial. In this paper, an attempt has been made to explore the role of place of articulation and vocal tract length in characterizing the different class of speech sounds. Formants and vocal tract length available for the production of each class of sound are extracted from the region of transition from consonant burst to the rising profile of the immediate following vowel. These features along with their statistical variations are considered for the analysis. Based on the non-linear nature of the features Random Forest (RF) is used for the classification. From the results, it is observed that the proposed features are efficient in discriminating the class of consonants: velar and palatal, palatal and retroflex and palatal and labial sounds with an accuracy of 92.9%, 93.83 and 94.07 respectively. © 2020, Springer Nature Singapore Pte Ltd.
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    Estimation of Tyre Pressure from the Characteristics of the Wheel: An Image Processing Approach
    (Springer, 2020) Vineeth Reddy, V.B.; Ananda Rao, H.; Yeshwanth, A.; Ramteke, P.B.; Koolagudi, S.G.
    Improper tyre pressure is a safety issue that falls prey to ignorance of users. But a drop in tyre pressure can result in the reduction of mileage, tyre life, vehicle safety and performance. In this paper, an approach is proposed to measure the tyre pressure from the image of the wheel. The tyre pressure is classified into under pressure and normal pressure using load index, tyre type, tyre position and ratio of compressed and uncompressed tyre radius. The efficiency of the feature is evaluated using three classifiers namely Random Forest, AdaBoost and Artificial Neural Networks. It is observed that the ratio of radii plays a major role in classifying the tyres. The proposed system can be used to obtain a rough idea on whether the tyre should be refilled or not. © 2020, Springer Nature Singapore Pte Ltd.