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@article{181810,
        author = {JIBY MARIYA JOSE},
        title = {An Efficient Sarcasm Detection in Audio Using Parameter-Reduced Depthwise CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {6000-6024},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181810},
        abstract = {In this study, we implement a lightweight CNN for sarcasm detection using audio input. To achieve this goal, we propose DepthFire block. We propose a lightweight version of the traditional Depthwise convolution layer that focuses on reduced memory. Unlike the traditional depthwise convolution layer that focuses on reducing the memory requirements of the entire architecture, our solution offers a specific and targeted approach that specifically reduces the memory requirements of the depthwise convolution layer through parameter reduction.We evaluated the impact of its energy consumption and the performance of our pro- posed solution with other existing solutions and on different activations, pooling functions and datasets.We further tested the applicability of the solution on 2D input.And our solution obtained 82.98 percent model size reduction as compared to MobileNetV2 and 58.94 percent as compared to MobileNetV3 small with a energy reduction of 56.48 percent on CIFAR10 dataset.},
        keywords = {Energy-Efficient CNN Design, Lightweight CNN Design, Sarcastic Speech Emotion Recognition, Deepfake-Detection, Brain-Tumour Detection, Energy-Efficiency, Reduce Training Time.},
        month = {June},
        }