Browsing by Author "Barkur, R."

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    EnsembleWave: An ensembled approach for Automatic Speech Emotion Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2022) Barkur, R.; Deepansh; I Suresh, D.; Mahesh Kumar, T.N.; Narasimhadhan, A.V.
    Accurate recognition of emotions from speech and understanding the determining factors behind the judgment can improve the machine's decision-making quality. Current state-of-the-art architectures have focused on either deep learning-based approaches or hand-engineered features. As a result, models fail in gathering complete contextual information and weak generalization across different datasets. This paper presents an end-to-end ensemble-based deep learning architecture that examines raw speech signals and classifies them into the four basic emotions - Sad, Angry, Happy, and Neutral. The proposed EnsembleWave architecture incorporates Attention Wavenet and hand-engineered feature extraction to assimilate a larger field-of-view and capture dataset independent characteristics. The model has achieved an overall accuracy of 98%, 85%, 74%, and 99%, on the four famous Speech Emotion Recognition (SER) datasets - EMO-DB, SAVEE, CREMA-D, and TESS, respectively, outperforming the state-of-the-art techniques both quantitatively and qualitatively. The proposed architecture can also learn the generalized categorization of emotions across different datasets. The python source code of the proposed model will be available at https://github.com/deepanshi-s/EnsembleWave © 2022 IEEE.
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    RSCDNet: A Robust Deep Learning Architecture for Change Detection From Bi-Temporal High Resolution Remote Sensing Images
    (Institute of Electrical and Electronics Engineers Inc., 2023) Deepanshi; Barkur, R.; Suresh, D.; Lal, S.; Chintala, C.S.; Diwakar, P.G.
    Accurate change detection from high-resolution satellite and aerial images is of great significance in remote sensing for precise comprehension of Land cover (LC) variations. The current methods compromise with the spatial context; hence, they fail to detect and delineate small change areas and are unable to capture the difference between features of the bi-temporal images. This paper proposes Remote Sensing Change Detection Network (RSCDNet) - a robust end-to-end deep learning architecture for pixel-wise change detection from bi-temporal high-resolution remote-sensing (HRRS) images. The proposed RSCDNet model is based on an encoder-decoder framework integrated with the Modified Self-Attention (MSA) andthe Gated Linear Atrous Spatial Pyramid Pooling (GL-ASPP) blocks; both efficient mechanisms to regulate the field-of-view while finding the most suitable trade-off between accurate localization and context assimilation. The paper documents the design and development of the proposed RSCDNet model and compares its qualitative and quantitative results with state-of-the-art HRRS change detection architectures. The above mentioned novelties in the proposed architecture resulted in an F1-score of 98%, 98%, 88%, and 75% on the four publicly available HRRS datasets namely, Staza-Tisadob, Onera, CD-LEVIR, and WHU. In addition to the improvement in the performance metrics, the strategic connections in the proposed GL-ASPP and MSA units significantly reduce the prediction time per image (PTPI) and provide robustness against perturbations. Experimental results yield that the proposed RSCDNet model outperforms the most recent change detection benchmark models on all four HRRS datasets. © 2017 IEEE.