Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Koolagudi, S.G.Subject: search.filters.subject.Economic and social effects

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    Explainable hate speech detection using LIME
    (Springer, 2024) Imbwaga, J.L.; Chittaragi, N.B.; Koolagudi, S.G.
    Free speech is essential, but it can conflict with protecting marginalized groups from harm caused by hate speech. Social media platforms have become breeding grounds for this harmful content. While studies exist to detect hate speech, there are significant research gaps. First, most studies used text data instead of other modalities such as videos or audio. Second, most studies explored traditional machine learning algorithms. However, due to the increase in complexities of computational tasks, there is need to employ complex techniques and methodologies. Third, majority of the research studies have either been evaluated using very few evaluation metrics or not statistically evaluated at all. Lastly, due to the opaque, black-box nature of the complex classifiers, there is need to use explainability techniques. This research aims to address these gaps by detecting hate speech in English and Kiswahili languages using videos manually collected from YouTube. The videos were converted to text and used to train various classifiers. The performance of these classifiers was evaluated using various evaluation and statistical measurements. The experimental results suggest that the random forest classifier achieved the highest results for both languages across all evaluation measurements compared to all classifiers used. The results for English language were: accuracy 98%, AUC 96%, precision 99%, recall 97%, F1 98%, specificity 98% and MCC 96% while the results for Kiswahili language were: accuracy 90%, AUC 94%, precision 93%, recall 92%, F1 94%, specificity 87% and MCC 75%. These results suggest that the random forest classifier is robust, effective and efficient in detecting hate speech in any language. This also implies that the classifier is reliable in detecting hate speech and other related problems in social media. However, to understand the classifiers’ decision-making process, we used the Local Interpretable Model-agnostic Explanations (LIME) technique to explain the predictions achieved by the random forest classifier. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
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    Rare sound event detection using superlets and a convolutional TDPANet
    (Springer Science and Business Media Deutschland GmbH, 2025) Pandey, G.; Koolagudi, S.G.
    Rare Sound Event Detection (RSED) focuses on identifying infrequent but significant sound events in audio recordings with precise onset and offset times. It is crucial for applications like surveillance, healthcare, and environmental monitoring. An essential component in RSED systems is extracting effective time-frequency representation as input features. These features capture short, transient acoustic events in an audio input recording, even in noisy and complex environments. Most existing approaches to this RSED problem rely on input features as time-frequency representations, such as the Mel spectrogram, Constant-Q Transform (CQT), and Continuous Wavelet Transform (CWT). However, these approaches often suffer from resolution trade-offs between frequency and time. This trade-off limits their ability to precisely capture the fine-grained details needed to detect these events in complex acoustic environments. To overcome these limitations, we introduce superlets, a novel time-frequency representation that offers super-resolution in both time and frequency domains. To process the high-resolution Superlet features, we have also proposed a Convolutional Temporal Dilated Pyramid Attention Network (TDPANet). This novel neural network architecture incorporates convolutional feature extraction, dilated temporal modeling, multi-scale temporal pooling, and temporal attention mechanisms to enhance event detection accuracy. We evaluate our method on the DCASE 2017 Task 2 rare sound event dataset, which includes isolated sound events and real-world acoustic scenes. Experimental results show that our proposed method significantly outperforms state-of-the-art techniques, achieving an Error Rate (ER) of 0.15 and an F1-score of 92.3%, demonstrating its effectiveness in detecting rare sound events. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.