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@article{193989,
        author = {POLICE SAINATH REDDY and MUKKANTI SHIVAPRASAD REDDY and YALAM LOKESH and MAHALAKSHIMI},
        title = {Audio Manipulation & Event Classification with Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6603-6612},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193989},
        abstract = {With the rapid growth of multimedia content across digital platforms, audio data has become a critical source of contextual and environmental information. This research presents an intelligent deep learning framework for audio manipulation and environmental sound event classification, aimed at automatically identifying, enhancing, and transforming real-world audio signals. Unlike traditional signal-processing-based systems that rely heavily on handcrafted features, the proposed approach leverages deep neural networks to learn discriminative audio representations directly from data.
The system integrates multiple processing stages, including audio preprocessing, feature extraction using Mel-spectrograms, noise manipulation, and deep learning-based event classification. A hybrid architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) is employed to capture both spatial-frequency patterns and temporal dependencies in audio signals. Experimental evaluation on benchmark environmental sound datasets demonstrates strong classification accuracy and robustness under noisy conditions. The results validate that deep learning-driven audio understanding provides an effective and scalable solution for intelligent sound analysis applications.},
        keywords = {Audio Event Classification, Deep Learning, CNN, RNN, Mel-Spectrogram, Sound Recognition, Audio Signal Processing.},
        month = {March},
        }